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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>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
Figure 4: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case against averaged data from healthy subjects-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>It is significant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the<br> asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-<br> proximately ¡50o, resulting in a fractional order of n »=</p> <p>This observation suggests that a combined efect of more than one fractal order is present in the<br> lungs and that it leads naturally to values closer to measured data in the low<br> frequency range. In other words, the symmetric tree representation does not<br> suffice to obtain a good fit between the model and the measured impedance<br> data. Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4</p>
Figure 1: EDL structure for p-Si-TOWARD THE PHYSICAL BASIS OF COMPLEX SYSTEMS: DIELECTRIC ANALYSIS OF POROUS SILICON NANOCHANNELS IN THE ELECTRICAL DOUBLE LAYER LENGTH RANGE
<p>Figure 1: EDL structure for p-Si (SCL negative charged,²F < ²FR )/aqueous<br> solvent interface. The electrostatic potential and charged atoms in solvent<br> distributions vs the distance z from the wall.</p>
Fig. 4 in M Or P Ho L O Gi Ca L Va R Iati On An D P Op U Lat Io N Structure Of The Natterjack Toad, Epidalea Calamita, In Northern Part Of The Range In Belarus
Fig. 4. Variation of the ratios males and females in the Natterjack toad Epidalea calamita population during the activity season in Belarus.
Fig. 3 in M Or P Ho L O Gi Ca L Va R Iati On An D P Op U Lat Io N Structure Of The Natterjack Toad, Epidalea Calamita, In Northern Part Of The Range In Belarus
Fig. 3. The color differences of males and females Natterjack toad Epidalea calamita in population of Belarus.
Fig. 3 in M Or P Ho L O Gi Ca L Va R Iati On An D P Op U Lat Io N Structure Of The Natterjack Toad, Epidalea Calamita, In Northern Part Of The Range In Belarus
Fig. 3. The color differences of males and females Natterjack toad Epidalea calamita in population of Belarus.
Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA
<p>Data and code necessary to replicate the findings from the manuscript titled "Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA".</p> <p><span>Abstract:</span><strong><span> </span></strong><span>Understanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to: (1) assess community resource use among streams fed by different hydrologic sources; (2) explore how variability in resource use relates to feeding strategies; and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice-fed streams were largely supported by the gold alga <em>Hydrurus</em>. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold-water species under climate change by maintaining a longer growing season for this high-quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggests high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.</span></p>
Linked collectors and determiners for: A new trapdoor spider species from the southern Coast Ranges of California (Mygalomorphae, Antrodiaetidae, Aliatypus coylei, sp. nov,), including consideration of mitochondrial phylogeographic structuring.
Natural history specimen data linked to collectors and determiners held within, "A new trapdoor spider species from the southern Coast Ranges of California (Mygalomorphae, Antrodiaetidae, Aliatypus coylei, sp. nov,), including consideration of mitochondrial phylogeographic structuring". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/676b9938-e0f2-4b00-8814-f40178c9fefd">https://bionomia.net/dataset/676b9938-e0f2-4b00-8814-f40178c9fefd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/676b9938-e0f2-4b00-8814-f40178c9fefd">https://gbif.org/dataset/676b9938-e0f2-4b00-8814-f40178c9fefd</a>. Formatted as a Frictionless Data package.
Fig. 7 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 7 – Comparison of the sizes of foraging areas of polycalic colonies and supercolonies of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges: a, Crimea, b, – Rostov-on-Don, c, Tashkent.
Fig. 6 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 6 – Average values of the average size of the foraging areas of Crematogaster subdentata (a) and Lasius neglectus (b) in the primary and secondary ranges. C – Crimea, T – Tashkent, R – Rostov-on-Don.
Fig. 5 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 5 – Relation of tree and shrub species visited by Crematogaster subdentata (a, Crimea, b, Rostov-on-Don, c, Tashkent) and Lasius neglectus (d, – Crimea, e, – Rostov-on-Don, f, – Tashkent). Trees: Ac – Acer sp., Ah – Aesculus hippocastanum, Aj – Albizia julibrissin, Al – Ailanthus altissima, An – Acer negundo, Cl – Cedrus libani, Co – Cydonia oblonga, Cs – Cupressus sempervirens, Ea – Elaeagnus angustifolia, Fe – Fraxinus excelsior, Fr – Fraxinus sp., Gl – Gleditsia triacanta, Jr – Juglans regia, M – Morus sp., Md – Malus domestica, Mn – Morus nigra, Pa – Prunus americana, Pb - Pinus brutia, Pc – Prunus cerasus, Pd – Prunus domestica, Pi – Pinus pallasiana, Pp – Populus niger, Po – Populus alba, Pr – Prunus cerasifera, Ps – Prunus spinosa, Py - Populus pyramidalis, Ra – Robinia pseudoacacia, Sa – Salix sp., Sj – Styphnolobium japonicum, Tl – Tilia sp., Ul – Ulmus sp., Us – Ulmus laevis.
Fig. 1 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 1 – Distribution of Crematogaster subdentata and Lasius neglectus in Tashkent C. subdentata, polycalic colonies, C. subdentata, monocalic colonies, L. neglectus,> 20 nests per 100 m, L. neglectus, 10-20 nests per 100 m, L. neglectus, <10 nests per 100 m.
Fig. 2 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 2 – Scheme of the foraging areas of Lasius neglectus in Rostov-on-Don species; trees: Pc – Prunus cerasus, Ps – Prunus spinosa, Ul – Ulmus sp.
Fig. 4 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 4 – Scheme of the foraging areas of Crematogaster subdentata in Crimea (A) and Tashkent (B) large accessible nests of C. subdentata in buildings and outside; inaccessible nests of C. subdentata in buildings; trees: Co – Cydonia oblonga, Jr – Juglans regia, M – Morus sp., Md – Malus domestica, Mn – Morus nigra, Pa – Prunus americana, Pd – Prunus domestica.
figure 1 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 1 The area surveyed for collection of otter samples (40° 40' N, 39° 37' N). Red spots indicate the location of the collected samples. The blue lines highlight the main rivers (order 1) and their tributaries (order 2, 3 and 4 according to waterway hierarchy). The continuous red lines represent regional boundaries. In the inset, the current otter distribution (inferred from Balestrieri et al., 2016, modified) is reported in orange and the study area is defined by the black bold square.
figure 4 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 4 Principal Component Analysis (pca) performed on microsatellite genotypes (dots). Circles show the well-defined spatial groups. A) pca according to the belonging of genotypes to the six river basins: the Cilento basin (green dots); the Agri basin (pink dots); the Sinni basin (blue dots); the Lao basin (red dots); the Basento basin (orange dots); the Abatemarco basin (violet dots); black dots indicate the samples outside of the main river basins. Dashed line indicates geographically contiguous but genetically different genotypes. B) pca according to clusters inferred by STRUCTURE: genotypes assigned unambiguously to K2 (green dots), to K3 (yellow dots), to K5 (violet dots). Grey dots represent samples with mixed genotypes assignable to K1 and K4.
figure 3 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 3 Genetic structure and distribution of the Italian otter genotypes in the study area. A) Estimated population structure based on the analysis of 11 microsatellite loci according to STRUCTURE (K = 5). Each bar represents a sample analysed. B) Geographic visualisation of genotypes in the study area performed using QGIS 3.4.1 software with base layers acquired from http://www.pnc.miniambiente. it/. Each circle represents a sample analysed. The colours indicate the percentage of assignment of an individual to each cluster: in blue, K1; in green, K2; in orange, K3; in red, K4; in violet, K5. The bold blue lines highlight the main rivers, while the tiny blue lines show all other waterways.
figure 6 Mantel test for A in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 6 Mantel test for A) the correlation between geographic distance (GGDsq) and genetic distance (LinGD) (Rxy = 0.264, P = 0.0001) and for B) the correlation between resistance distance (a measure of ecological distance) (ECO500) and LinGD (Rxy = 0.217, P = 0.0001).
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