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Histological Dataset for Microvascular Segmentation of Tissue-Engineered Vascular Grafts
<p><strong>Objectives: </strong>The pursuit of understanding vascular tissue regeneration within tissue-engineered vascular grafts (TEVGs) is of paramount importance due to the critical role these grafts play in replacing damaged or diseased blood vessels. TEVGs offer a promising alternative to traditional grafts, with the potential to integrate into the host's tissue and support the natural regenerative processes. However, challenges such as thrombosis, inflammation, and the need for grafts that can adapt to the dynamic biological environment remain. By studying the regenerative processes in TEVGs, researchers can gain insights into the mechanisms that underpin successful graft integration and function, which is essential for improving patient outcomes in vascular surgeries. This dataset, with its detailed annotations of histological features, provides a valuable resource for developing and refining machine-learning models that can analyze and predict patterns of vascular tissue regeneration. The ability to accurately segment and quantify microvessels and immune cells in regenerated arteries is a significant step forward in distinguishing between physiological and pathological regeneration, ultimately contributing to the design of more effective and reliable TEVGs for clinical use.</p> <p><strong>Ethical Approval: </strong>Experimental strategy of the study is described in detail in <a href="https://www.mdpi.com/2073-4360/14/23/5149" target="_blank" rel="noopener">[1]</a> and <a href="https://www.mdpi.com/1422-0067/24/10/8540" target="_blank" rel="noopener">[2]</a>. The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the Local Ethical Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia, protocol code 2020/06, date of approval: 19 February 2020). Animal experiments were performed in accordance with the European Convention for the Protection of Vertebrate Animals (Strasbourg, 1986) and Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes. For the implantation, we used female Edilbay sheep of 42–45 kg body weight which were received from the Animal Core Facility of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia) and selected for the surgery by Doppler ultrasonography to identify those having carotid artery diameter of 4.0 ± 0.2 mm.</p> <p><strong>Description: </strong>The dataset comprises a collection of Whole Slide Images (WSIs) obtained from biodegradable TEVGs implanted into the carotid arteries of 20 sheep. A total of 104 WSIs were acquired, each measuring an average size of 135,000 x 123,000 pixels. These WSIs were stained using Hematoxylin and Eosin (H&E), a common practice for highlighting the structure of tissue sections, which facilitates the detailed examination of histological features. These WSIs were automatically sliced into 99,831 patches of 3,000 x 3,000 pixels and subsequently filtered, resulting in 1,401 selected patches for manual annotation.</p> <p><strong>Annotation Method:</strong> Two pathologists independently selected and meticulously annotated the 1401 patches, identifying nine distinct histological features associated with vascular tissue regeneration. These features include <em>arteriole lumen (AL)</em>, <em>arteriole media (AM)</em>, <em>arteriole adventitia (AA)</em>, <em>venule lumen (VL)</em>, <em>venule wall (VW)</em>, <em>capillary lumen (CL)</em>, <em>capillary wall (CW)</em>, <em>immune cells (IC)</em>, and <em>nerve trunks (NT)</em>. The annotations were performed using binary masks, delineating each feature within the patches. Subsequently, a senior pathologist conducted a triple verification process, reviewing and refining the annotations to ensure accuracy and consistency. The annotations are provided in the form of binary masks, meticulously defined for each feature within the patches.</p> <p><strong>Dataset Split:</strong> Given the limited number of subjects studied, comprising 20 sheep, we employed a 5-fold cross-validation technique to split our dataset. This method was chosen because it allows for the efficient use of limited data, ensuring that each observation has the opportunity to be used in both the training and testing sets, thus reducing bias and providing a more accurate estimate of the model's performance. In this approach, each fold involved 16 sheep for training and the remaining 4 for testing (see <em>Table 1</em> and <em>Figure 3</em>). This partitioning scheme was consistently applied to maintain the integrity of subject groups within each subset and to prevent data leakage. The 5-fold cross-validation is particularly beneficial for our study's objectives as it maximizes the training data available for developing robust machine learning models while also ensuring that the models are tested on unseen data, thereby enhancing the generalizability of our findings.</p> <p><strong>Access to the Study:</strong> Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories:</p> <ul> <li><strong>Source code:</strong> <a href="https://github.com/ViacheslavDanilov/histology_segmentation" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/histology_segmentation</a></li> <li><strong>Dataset:</strong> <a href="https://doi.org/10.5281/zenodo.10838384" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838384</a></li> <li><strong>Models:</strong> <a href="https://doi.org/10.5281/zenodo.10838431" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838431</a></li> </ul> <div> </div> <div><em><strong>Table 1.</strong> Patch and feature distributions across folds and subsets</em> <table> <tbody> <tr> <td> <p><strong>Fold</strong></p> </td> <td> <p><strong>Subset</strong></p> </td> <td> <p><strong>Patches</strong></p> </td> <td> <p><strong>AL</strong></p> </td> <td> <p><strong>AM</strong></p> </td> <td> <p><strong>AA</strong></p> </td> <td> <p><strong>VL</strong></p> </td> <td> <p><strong>VW</strong></p> </td> <td> <p><strong>CL</strong></p> </td> <td> <p><strong>CW</strong></p> </td> <td> <p><strong>IC</strong></p> </td> <td> <p><strong>NT</strong></p> </td> <td> <p><strong>Total </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Train</p> </td> <td> <p>1168</p> </td> <td> <p>510</p> </td> <td> <p>512</p> </td> <td> <p>220</p> </td> <td> <p>675</p> </td> <td> <p>648</p> </td> <td> <p>770</p> </td> <td> <p>765</p> </td> <td> <p>409</p> </td> <td> <p>448</p> </td> <td> <p>4957</p> </td> </tr> <tr> <td>1</td> <td> <p>Test</p> </td> <td> <p>233</p> </td> <td> <p>81</p> </td> <td> <p>84</p> </td> <td> <p>36</p> </td> <td> <p>186</p> </td> <td> <p>169</p> </td> <td> <p>178</p> </td> <td> <p>182</p> </td> <td> <p>91</p> </td> <td> <p>25</p> </td> <td> <p>1032</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>Train</p> </td> <td> <p>1053</p> </td> <td> <p>406</p> </td> <td> <p>411</p> </td> <td> <p>179</p> </td> <td> <p>678</p> </td> <td> <p>638</p> </td> <td> <p>743</p> </td> <td> <p>746</p> </td> <td> <p>423</p> </td> <td> <p>315</p> </td> <td> <p>4539</p> </td> </tr> <tr> <td>2</td> <td> <p>Test</p> </td> <td> <p>348</p> </td> <td> <p>185</p> </td> <td> <p>185</p> </td> <td> <p>77</p> </td> <td> <p>183</p> </td> <td> <p>179</p> </td> <td> <p>205</p> </td> <td> <p>201</p> </td> <td> <p>77</p> </td> <td> <p>158</p> </td> <td> <p>1450</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>Train</p> </td> <td> <p>1127</p> </td> <td> <p>507</p> </td> <td> <p>511</p> </td> <td> <p>222</p> </td> <td> <p>743</p> </td> <td> <p>702</p> </td> <td> <p>759</p> </td> <td> <p>760</p> </td> <td> <p>299</p> </td> <td> <p>423</p> </td> <td> <p>4926</p> </td> </tr> <tr> <td>3</td> <td> <p>Test</p> </td> <td> <p>274</p> </td> <td> <p>84</p> </td> <td> <p>85</p> </td> <td> <p>34</p> </td> <td> <p>118</p> </td> <td> <p>115</p> </td> <td> <p>189</p> </td> <td> <p>187</p> </td> <td> <p>201</p> </td> <td> <p>50</p> </td> <td> <p>1063</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>Train</p> </td> <td> <p>1064</p> </td> <td> <p>466</p> </td> <td> <p>472</p> </td> <td> <p>199</p> </td> <td> <p>611</p> </td> <td> <p>566</p> </td> <td> <p>759</p> </td> <td> <p>758</p> </td> <td> <p>423</p> </td> <td> <p>291</p> </td> <td> <p>4545</p> </td> </tr> <tr> <td>4</td> <td> <p>Test</p> </td> <td> <p>337</p> </td> <td> <p>125</p> </td> <td> <p>124</p> </td> <td> <p>57</p> </td> <td> <p>250</p> </td> <td> <p>251</p> </td> <td> <p>189</p> </td> <td> <p>189</p> </td> <td> <p>77</p> </td> <td> <p>182</p> </td> <td> <p>1444</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>Train</p> </td> <td> <p>1192</p> </td> <td> <p>475</p> </td> <td> <p>478</p> </td> <td> <p>204</p> </td> <td> <p>737</p> </td> <td> <p>714</p> </td> <td> <p>761</p> </td> <td> <p>759</p> </td> <td> <p>446</p> </td> <td> <p>415</p> </td> <td> <p>4989</p> </td> </tr> <tr> <td>5</td> <td> <p>Test</p> </td> <td> <p>209</p> </td> <td> <p>116</p> </td> <td> <p>118</p> </td> <td> <p>52</p> </td> <td> <p>124</p> </td> <td> <p>103</p> </td> <td> <p>187</p> </td> <td> <p>188</p> </td> <td> <p>54</p> </td> <td> <p>58</p> </td> <td> <p>1000</p> </td> </tr> </tbody> </table> </div> <p> </p>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization
<h1><strong>Abstract</strong></h1> <p>This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. <br><br>SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms.<br>To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available <a href="https://github.com/remaro-network/SubPipe-dataset">online.</a></p> <p>On Zenodo we provide <em>three</em> versions for SubPipe. One is the full version (<strong>SubPipe.zip</strong>, ~80GB unzipped) and two subsamples: <strong>SubPipeMini.zip</strong>, ~12GB unzipped and <strong>SubPipeMini2.zip</strong>, ~16GB unzipped. Both subsamples are only parts of the entire dataset (SubPipe.zip). SubPipeMini is a subset, containing semantic segmentation data, and it has interesting camera data of the underwater pipeline. On the other hand, SubPipeMini2 is mainly focused on underwater side-scan sonar images of the seabed including ground truth object detection bounding boxes of the pipeline.</p> <p><strong>For (re-)using/publishing SubPipe, please include the following copyright text:</strong></p> <p><em><strong>SubPipe</strong> is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p><em>More information about OceanScan-MST can be found at </em><a href="https://www.oceanscan-mst.com/"><em>this link</em></a><em>.</em></p> <h1><strong>Cam0 — GoPro Hero 10</strong></h1> <h4>Camera parameters:</h4> <ul> <li>Resolution: 1520×2704</li> <li>fx = 1612.36</li> <li>fy = 1622.56</li> <li>cx = 1365.43</li> <li>cy = 741.27</li> <li>k1,k2, p1, p2 = [−0.247, 0.0869, −0.006, 0.001]</li> </ul> <h1><strong>Side-scan Sonars</strong></h1> <p>Each sonar image was created after 20 “ping” (after every 20 new lines) which corresponds to approx. ~1 image / second.</p> <p>Regarding the object detection annotations, we provide both COCO and YOLO formats for each annotation. A single COCO annotation file is provided per each chunk and per each frequency (low frequency vs. high frequency), whereas the YOLO annotations are provided for each SSS image file.</p> <p>Metadata about the side-scan sonar images contained in this dataset:</p> <table> <tbody> <tr> <td><strong>Images for object detection</strong></td> <td> </td> </tr> <tr> <td># Low Frequency (LF):</td> <td> 5000</td> </tr> <tr> <td>LF image size:</td> <td>2500 × 500</td> </tr> <tr> <td># High Frequency (HF):</td> <td> 5030</td> </tr> <tr> <td>HF Image size</td> <td>5000 × 500</td> </tr> <tr> <td><strong>Total number of images:</strong></td> <td>10030</td> </tr> <tr> <td><strong>Annotations</strong><strong><br></strong></td> <td> </td> </tr> <tr> <td># Low Frequency:</td> <td> 3163</td> </tr> <tr> <td># High Frequency:</td> <td> 3172</td> </tr> <tr> <td><strong>Total number of annotations:</strong></td> <td><strong> </strong>6335</td> </tr> </tbody> </table>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
OpenForensics: Multi-Face Forgery Detection And Segmentation In-The-Wild Dataset [V.1.0.0]
<p>OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page: https://sites.google.com/view/ltnghia/research/openforensics</p>
Dataset for Semantic Segmentation of Fishing Trajectories
<p>This is the dataset that was manually labelled by the author during his research work for the paper "Semantic Segmentation of AIS Trajectories for Detecting Complete Fishing Activities" in MDM 2022.</p>
Pre-training with simulated ultrasound images for breast mass segmentation and classification - dataset
<p>Dataset assosiated with the MICCAI Workshop on Data Engineering in Medical Imaging paper: "Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification"</p>
Dataset for Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation
<p>This repo includes the self-generated dataset as well as the pre-trained models .</p> <p>ISVC 2020 - 15th International Symposium on Visual Computing</p> <p>Paper: Overscan Detection in Digitized Analog Filmsby Precise Sprocket Hole Segmentation</p> <p> </p> <p>Acknowledgement:</p> <p>Visual History of the Holocaust: Rethinking Curation in the Digital Age. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Grant Agreement 822670.</p> <p>https://www.vhh-project.eu</p> <p> </p>
Datasets and Models for Historical Newspaper Article Segmentation
<p>This record contains the datasets and models used and produced for the work reported in the paper "<em>Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers</em>" (<a href="https://infoscience.epfl.ch/record/282863?ln=en">link</a>).</p> <p>Please cite this paper if you are using the models/datasets or find it relevant to your research:</p> <pre><code>@article{barman_combining_2020, title = {{Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers}}, author = {Raphaël Barman and Maud Ehrmann and Simon Clematide and Sofia Ares Oliveira and Frédéric Kaplan}, journal= {Journal of Data Mining \& Digital Humanities}, volume= {HistoInformatics} DOI = {10.5281/zenodo.4065271}, year = {2021}, url = {https://jdmdh.episciences.org/7097}, }</code></pre> <p><br> <strong>Please note that this record contains data under different licenses.</strong><br> <br> <strong>1. DATA</strong></p> <ul> <li><strong>Annotations (json files)</strong>: JSON files contains image annotations, with one file per newspaper containing region annotations (label and coordinates) in VIA format. The following licenses apply: <ul> <li> luxwort.json: those annotations are under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 license</a>. Please refer to the right statement specified for each image in the file.</li> <li>GDL.json, IMP.json and JDG.json: those annotations are under a <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">CC BY-SA 4.0 license</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Image files: </strong>The archive images.zip contains the Swiss titles image files (GDL, IMP, JDG) used for the experiments described in the paper. Those images are under copyright (property of the journal <em>Le Temps </em>and of <em>ArcInfo</em>) and can be used <em>for academic research or educational purposes only</em>. Redistribution, publication or commercial use are not permitted. These terms of use are similar to the following right statement: <a href="http://rightsstatements.org/vocab/InC-EDU/1.0/">http://rightsstatements.org/vocab/InC-EDU/1.0/</a></li> </ul> <p> </p> <p><strong>2. MODELS</strong></p> <p>Some of the best models are released under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> license (they are also available as assets of the current Github <a href="https://github.com/dhlab-epfl/dhSegment-text/releases/tag/0.1">release</a>).</p> <ul> <li><strong>JDG_flair-FT</strong>: this model was trained on JDG using french Flair and FastText embeddings. It is able to predict the four classes presented in the paper (<code>Serial</code>, <code>Weather</code>, <code>Death notice</code> and <code>Stocks</code>).</li> <li><strong>Luxwort_obituary_flair-bpemb</strong>: this model was trained on Luxwort using multilingual Flair and Byte-pair embeddings. It is able to predict the <code>Death notice</code> class.</li> <li><strong>Luxwort_obituary_flair-FT_indomain</strong>: this model was trained on Luxwort using in-domain Flair and FastText embeddings (trained on Luxwort data). It is also able to predict the <code>Death notice</code> class.</li> </ul> <p>Those models can be used to predict probabilities on new images using the same code as in the original <a href="https://github.com/dhlab-epfl/dhSegment">dhSegment</a> repository. One needs to adjust three parameters to the <code>predict</code> function: 1) <code>embeddings_path</code> (the path to the embeddings list), 2) <code>embeddings_map_path</code>(the path to the compressed embedding map), and 3) <code>embeddings_dim</code> (the size of the embeddings).</p> <p>Please refer to the paper for further information or contact us.</p> <p> </p> <p><strong>3. CODE: </strong></p> <p><a href="https://github.com/dhlab-epfl/dhSegment-text">https://github.com/dhlab-epfl/dhSegment-text</a></p> <p><br> <strong>4. ACKNOWLEDGEMENTS</strong><br> We warmly thank the journal <a href="https://letemps.ch">Le Temps</a> (owner of <em>La Gazette de Lausanne</em> and the <em>Journal de Genève</em>) and the group <a href="https://www.arcinfo.ch/">ArcInfo</a> (owner of <em>L'Impartial</em>) for accepting to share the related datasets for academic purposes. We also thank the <a href="https://bnl.public.lu/fr.html">National Library of Luxembourg</a> for its support with all steps related to the <em>Luxemburger Wort</em> annotation release.<br> This work was realized in the context of the <a href="https://impresso-project.ch"><em>impresso</em> - Media Monitoring of the Past</a> project and supported by the Swiss National Science Foundation under grant CR- SII5_173719.<br> <br> <strong>5. CONTACT</strong><br> Maud Ehrmann (EPFL-DHLAB)<br> Simon Clematide (UZH)</p>
Cu dataset – A copper ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p>
Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell
<p>This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).</p><p>The full FIB-SEM dataset is deposited to EMPIAR (<a href="https://www.ebi.ac.uk/empiar">https://www.ebi.ac.uk/empiar</a>, EMPIAR-11746). </p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
SEMFIRE forest dataset for semantic segmentation and data augmentation
<p><strong>SEMFIRE Datasets (Forest environment dataset)</strong></p> <p>These datasets are used for semantic segmentation and data augmentation and contain various forestry scenes. They were collected as part of the research work conducted by the Institute of Systems and Robotics, University of Coimbra <a href="https://isr.uc.pt/index.php/people?task=showprojects.show&idProject=203">team</a> within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. <a href="http://semfire.ingeniarius.pt/">CENTRO-01-0247-FEDER-032691</a>) research project coordinated by <a href="https://ingeniarius.pt/">Ingeniarius Ltd.</a></p> <p>The semantic segmentation algorithms attempt to identify various semantic classes (e.g. background, live flammable materials, trunks, canopies etc.) in the images of the datasets.</p> <p>The datasets include diverse image types, e.g. original camera images and their labeled images. In total the SEMFIRE datasets include about 1700 image pairs. Each dataset includes corresponding .bag files.</p> <p>To launch those .bag files on your ROS environment, use the instructions on the following Github <a href="https://github.com/Forestry-Robotics-UC/fruc_rosbags">repository</a></p> <p>Description of<strong> </strong>each <strong>dataset:</strong></p> <ol> <li><strong>2019_2020_quinta_do_bolao_coimbra:</strong> Robot moving on a path through a forest environment</li> <li><strong>2020_ctcv_parking_lot_coimbra:</strong> Robot moving in a circle in a parking lot for testings</li> <li><strong>2020_sete_fontes_forest: </strong>A set of forest images acquired by hand-held apparatus</li> </ol> <p>Each <strong>dataset</strong> consists of following <strong>directories:</strong></p> <ol> <li><strong>images directory: </strong>diverse image types, e.g. original camera images and their labeled images</li> <li><strong>rosbags directory: </strong>.bag files, which correspond to the image directory</li> </ol> <p>Each <strong>images directory </strong>consists of following <strong>directories:</strong></p> <ul> <li><strong>img:</strong> original camera images</li> <li><strong>lbl:</strong> single channel images (ground truth) with corresponding labels for each image in<strong> img</strong></li> <li><strong>lbl_colored: </strong>camera <strong> </strong>images in <strong>lbl</strong> colorized according to different semantic classes (for more details see the datasets descriptions)</li> <li><strong>lbl_overlaid: </strong>camera images in <strong>img </strong>overlaid with corresponding labels (colored)</li> </ul> <p>Each <strong>rosbags directory </strong>contains .bag files with the following <strong>topics:</strong></p> <ul> <li><strong>2019_2020_quinta_do_bolao_coimbra_rosbags: </strong> <ul> <li>/back_lslidar_packet</li> <li>/dalsa_camera_720p/compressed</li> <li>/flir_ax8/compressed</li> <li>/front_lslidar_packet</li> <li>/gps_fix</li> <li>/gps_time</li> <li>/gps_vel</li> <li>/imu/data</li> <li>/realsense/aligned_depth_to_color/image_raw</li> <li>/realsense/color/camera_info</li> <li>/realsense/color/image_raw/compressed</li> <li>/realsense/depth/camera_info</li> <li>/realsense/depth/image_rect_raw/compressed</li> <li>/realsense/extrinsics/depth_to_color</li> </ul> </li> <li><strong>2020_ctcv_parking_lot_coimbra_rosbags:</strong> <ul> <li>/dalsa_camera_720p/compressed</li> <li>/gps_fix</li> <li>/gps_ime</li> <li>/fused_point_cloud</li> <li>/imu/data</li> <li>/imu/mag</li> <li>/imu/rpy</li> </ul> </li> <li><strong>2020_sete_fontes_forest_rosbags: </strong> <ul> <li>/realsense/camera_info</li> <li>/realsense/depth_compressed/compressedDepth</li> <li>/realsense/nir/left/compressed</li> <li>/realsense/nir/right/compressed</li> <li>/realsense/rgb/compressed</li> </ul> </li> </ul> <p>All datasets include a detailed description as a text file. In addition, they include a rosbag_info.txt file with a description for each ROS inside the .bag files as well as a description for each ROS topic.</p> <p> </p> <p>The following table shows the statistical description of typical portuguese woodland configurations with structured plantations of <em>Pinus pinaster </em>(<em>Pp, </em>pine trees) and <em>Eucalyptus globulus </em>(<em>Eg, </em>eucalyptus).</p> <table> <tbody> <tr> <td> </td> <td><strong>"Low density" structured plantation</strong></td> <td><strong>"High density" structured plantation</strong></td> </tr> <tr> <td><strong>Tree density (assuming plantation in rows spaced 3m apart in all cases)</strong></td> <td> <p><em>Eg</em>: 900 trees/ha</p> <p><em>Pp</em>: 450 trees/ha</p> </td> <td> <p><em>Eg</em>: 1400 trees/ha</p> <p><em>Pp</em>: 1250 trees/ha</p> </td> </tr> <tr> <td> <p><strong>Average heights and corresponding ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> </tr> <tr> <td> <p><strong>Maximum heights and corresponding fully-matured ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> </tr> <tr> <td> <p><strong>Diameter at chest level (DCL – 1,3m) of plantation trees (average/maximum)</strong></p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> </tr> <tr> <td> <p><strong>Natural density of herbaceous plants</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of bush and shrubbery</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of arboreal plants (not part of plantation)</strong></p> </td> <td> <p>5% of woodland area</p> </td> <td> <p>5% of woodland area</p> </td> </tr> </tbody> </table> <ul> </ul>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2
<p>This archive contains part 2 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is contains data collected from several different sources and distributed under a CC BY NC SA 4.0 license. Part 1 of the data is available <a href="https://zenodo.org/record/7051658">here</a>. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai .</p>
DOORS: Dataset fOr bOuldeRs Segmentation
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations and navigation. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets for these applications damps the research about data-driven algorithms. To tackle these challenges, the Dataset fOr bOuldeRs Segmentation (DOORS) has been designed. The dataset is thought to be useful for (but not limited to) boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets:</p> <ul> <li><strong>Regression: </strong>Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression.</li> <li><strong>Segmentation: </strong>Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model</li> </ul> <p>A detailed characterization of the statistical properties of the DOORS dataset and the description of the Blender setup used to generate it is visible in "DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup", by Mattia Pugliatti and Francesco Topputo, arXiv pre-print, Oct 2022. </p> <p> </p>
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
<p>The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.</p> <p> </p>
The Monk Line Segmentation (MLS) Dataset
<p>Overview</p> <p>The MLS dataset available from this page consists of 31 handwritten page scans. The dataset contains medieval, historical and contemporary manuscripts, and has the purpose of testing line-segmentation algorithms. The collection contains a wide variation of the common problems in handwriting recognition: lines with overlapping ascenders/descenders, slightly rotated scans and curved base lines. <br> </p> <p>Download</p> <p>The MLS dataset was collected from the Monk system as of Friday May 17 14:15:04 CEST 2013. It was collected by Lambert Schomaker in May 2013 at the Institution of Artificial Intelligence and Cognitive Engineering (ALICE), University of Gronigen. </p> <p>The tar.gz file contains the image dataset for historical manuscripts. For more details please refer to the README file in the tar.gz file. The dataset downloaded for research use only. © 2013 Copyright. <br> </p> <p>@INPROCEEDINGS{Surinta:2014:ICFHR,<br> author = {O. Surinta and M. Holtkamp and M. F. Karaaba and JP. van Oosten and L. R. B. Schomaker and M. A. Wiering},<br> title = {A* Path Planning for Line Segmentation of Handwritten Documents},<br> booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},<br> year = {2014},<br> month = {Sep},<br> pages = {175-180},<br> numpages = {6},<br> isbn = {978-1-4799-4335-7},<br> issn = {2167-6445},<br> publisher = {IEEE},<br> doi = {http://dx.doi.org/10.1109/ICFHR.2014.37},<br> }</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.