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282
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ShareScore release 0.7.1
Dataset results
282 results for “image segments”
Serial two-photon tomography (STPT) of the brain through bi-channel image registration and deep learning segmentation (BIRDS)
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Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
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Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data
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Sashimi: A toolkit for facilitating high-throughput organismal image segmentation using deep learning
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Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p> See paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation' for an explanation of how the models and datasets were created.</p> <p>The images are extracted from the following larger datasets using the RootPainter software:</p> <p>Nodules: http://doi.org/10.5281/zenodo.3753603</p> <p>Biopores: http://doi.org/10.5281/zenodo.3753969</p> <p>Roots: http://doi.org/10.5281/zenodo.3527713</p> <p> </p>
Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
FIGURE 4 in Automated segmentation of insect anatomy from micro-CT images using deep learning
FIGURE 4 Data augmentation. (a) Initial 2D image of a full head scan of an Atta texana ant specimen (original 1000 × 1000 px). Preprocessing is performed in two steps: (b) The image is cropped (original 520 × 520 px) around the brain area, keeping some of the muscles, nerves, and fibers that are close (or even attached) to the brain. The manual segmentation of the brain is indicated in blue. (c) Histogram equalization is used for additional augmentation, which enhances the contrast and projects the inner parts of the brain more clearly.
Images and Labels for Segmentation Studies in Microscopy
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Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images"
<p>Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images". </p> <p> </p> <p>image_prediction_pt1 and _pt2 have the same content as image_prediction.zip but split in two parts for faster downloading with Google Colab (to avoid time out)</p> <p> </p> <p>Contact</p> <p>Ricardo Dalagnol</p> <p>ricds@hotmail.com</p>
Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids
In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.
tree rings image segmentation original
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Boston & Corniolo Datasets - road segmentation - described in "An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images""
<p>In Corniolo.rar the masks (values {0,1}) are saved in the png files</p>
Test Dataset for 3D semantic image segmentation of the Liver and Tumor
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Hybrid Intelligence in Medical Image Segmentation
<p>Title of publication: Hybrid Intelligence in Medical Image Segmentation</p> <div>The original dataset contains x-rays and corresponding masks. Some masks are missing so it is advised to cross-reference the images and masks. The dataset link consisting of train and test is attached here, <br><br></div> <div><a title="Original URL: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data. Click or tap if you trust this link." href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.kaggle.com%2Fdatasets%2Fnikhilpandey360%2Fchest-xray-masks-and-labels%2Fdata&data=05%7C02%7CS.Oyelere%40exeter.ac.uk%7C80fa490cf7be41cf7a6508dd0069c919%7C912a5d77fb984eeeaf321334d8f04a53%7C0%7C0%7C638667176577007815%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Fai5kYk%2FhaVfJp%2FhtFOFQfYSqcoFcpDTtiq%2FWiktUYY%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data</a></div> <div> </div> <div>The OP had the following request:<br>It is requested that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA and Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China) and cite the following publications:<br>Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang YX, Lu PX, McDonald CJ. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713<br>Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990</div> <div> </div> <div>The dataset is split into test and validation also so the final ones are attached as : train data, test data link and </div> <div>validation data link :<br>The clinician masks are attached as: validation mask and testing mask.</div>
Data from: Segmentation of laterally symmetric overlapping objects: application to images of collective animal behavior
Video analysis is currently the main non-intrusive method for the study of collective behavior. However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for up to almost 90% of overlaps, and the mean Dice-Sørensen coefficient was around 0.83. This implies that this method is feasible in real-life applications such as toxicity testing.
Binary segmentation images for 3D segmentation model training
<p>Binary segmentation images for 3D segmentation model training</p>
Outputs from new methods for 3D+time image segmentation and tracking
<p>Segmentation and tracking of 3D+time images of artificially generates spheres. <br> The file named _20_frames_of_moving_spheres.avi is a 20-frame video of artificially generated spheres moving in time, the file <br> named _resulf_of_20_frames_of_moving_spheres.avi has the result of 4D segmentation, using our new segmentation methods, of the spheres (colored blue) moving in time, and the file _tracking_of_artificial_data_in_20_frames.mp4 has the tracking of these spheres. Additionally, the file named _one_moving_sphere.avi is also a 20-frame video of an artificially generated sphere moving in time, the file named _segmentation_result_one_moving_sphere.avi has the result of 4D segmentation of the sphere (colored blue) moving in time, and the file named _segmentation_result_one_moving_sphere_with_some_missing_spheres.avi has the result of 4D segmentation of the sphere (colored blue) moving in time when frames 5, 10, and 15 are missing in the file _one_moving_sphere.avi.</p>
Image Segmentation
<p>This is a proof of concept dataset collected using the method introduced in the following publication. See details here : https://arxiv.org/abs/2301.07807</p>
SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline
<p>SEM images raw data X1, X1, Y1, Y2, Z1 and Z2</p>
Patient Specific Virtual Reality for Simulation of Spine Procedures: an Intelligent Image Segmentation, Registration and 3-dimensional Visualization in a Unified Virtual Reality Workflow for Image Gui
ClinicalTrials.gov study NCT06714539. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
ScienceDex guides
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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.