NeurIPS 2022 Cell Segmentation Competition Dataset

<p>The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images.</p> <p>https://neurips22-cellseg.grand-challenge.org/</p> <p>Please cite the following paper if this dataset is used in your research.&nbsp;</p> <p>&nbsp;</p> <pre><code><span>@article</span><span>{</span><span>NeurIPS</span><span>-</span><span>CellSeg</span><span>,</span> <span>title</span> <span>=</span> <span>{</span><span>The</span> <span>Multi</span><span>-</span><span>modality</span> <span>Cell</span> <span>Segmentation</span> <span>Challenge</span><span>:</span> <span>Towards</span> <span>Universal</span> <span>Solutions</span><span>}</span><span>,</span> <span>author</span> <span>=</span> <span>{</span><span>Jun</span> <span>Ma</span> <span>and</span> <span>Ronald</span> <span>Xie</span> <span>and</span> <span>Shamini</span> <span>Ayyadhury</span> <span>and</span> <span>Cheng</span> <span>Ge</span> <span>and</span> <span>Anubha</span> <span>Gupta</span> <span>and</span> <span>Ritu</span> <span>Gupta</span> <span>and</span> <span>Song</span> <span>Gu</span> <span>and</span> <span>Yao</span> <span>Zhang</span> <span>and</span> <span>Gihun</span> <span>Lee</span> <span>and</span> <span>Joonkee</span> <span>Kim</span> <span>and</span> <span>Wei</span> <span>Lou</span> <span>and</span> <span>Haofeng</span> <span>Li</span> <span>and</span> <span>Eric</span> <span>Upschulte</span> <span>and</span> <span>Timo</span> <span>Dickscheid</span> <span>and</span> <span>Jos&eacute;</span> <span>Guilherme</span> <span>de</span> <span>Almeida</span> <span>and</span> <span>Yixin</span> <span>Wang</span> <span>and</span> <span>Lin</span> <span>Han</span> <span>and</span> <span>Xin</span> <span>Yang</span> <span>and</span> <span>Marco</span> <span>Labagnara</span> <span>and</span> <span>Vojislav</span> <span>Gligorovski</span> <span>and</span> <span>Maxime</span> <span>Scheder</span> <span>and</span> <span>Sahand</span> <span>Jamal</span> <span>Rahi</span> <span>and</span> <span>Carly</span> <span>Kempster</span> <span>and</span> <span>Alice</span> <span>Pollitt</span> <span>and</span> <span>Leon</span> <span>Espinosa</span> <span>and</span> <span>T&acirc;m</span> <span>Mignot</span> <span>and</span> <span>Jan</span> <span>Moritz</span> <span>Middeke</span> <span>and</span> <span>Jan</span><span>-</span><span>Niklas</span> <span>Eckardt</span> <span>and</span> <span>Wangkai</span> <span>Li</span> <span>and</span> <span>Zhaoyang</span> <span>Li</span> <span>and</span> <span>Xiaochen</span> <span>Cai</span> <span>and</span> <span>Bizhe</span> <span>Bai</span> <span>and</span> <span>Noah</span> <span>F</span><span>.</span> <span>Greenwald</span> <span>and</span> <span>David</span> <span>Van</span> <span>Valen</span> <span>and</span> <span>Erin</span> <span>Weisbart</span> <span>and</span> <span>Beth</span> <span>A</span><span>.</span> <span>Cimini</span> <span>and</span> <span>Trevor</span> <span>Cheung</span> <span>and</span> <span>Oscar</span> <span>Br&uuml;ck</span> <span>and</span> <span>Gary</span> <span>D</span><span>.</span> <span>Bader</span> <span>and</span> <span>Bo</span> <span>Wang</span><span>}</span><span>,</span> <span>journal</span> <span>=</span> <span>{</span><span>Nature</span> <span>Methods</span><span>}</span><span>,</span><span><br> &nbsp; &nbsp; &nbsp;volume={21},<br> pages={1103&ndash;1113},<br> <span>year</span> <span>=</span> <span>{</span><span>2024</span><span>}</span>,</span> <span>doi</span> <span>=</span> <span>{</span><span>https</span><span>:</span><span>//</span><span>doi</span><span>.</span><span>org</span><span>/</span><span>10.1038</span><span>/</span><span>s41592</span><span>-</span><span>024</span><span>-</span><span>02233</span><span>-</span><span>6</span><span>}</span> <span>}</span></code></pre> <p>&nbsp;</p> <p>This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning.</p> <p>&nbsp;</p> <p>The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats.</p> <p>&nbsp;</p> <p><strong>We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website!</strong></p> <p>&nbsp;</p> <p><strong>Dataset License: CC-BY-NC-ND</strong></p>