16 research outputs found
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
No full text<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>
Author response
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