MoNuSeg

Description

The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box.

Dataset Structure

The dataset is organized into train and test splits, consistent with the original dataset structure. Each split contains data in a tabular format with the following four columns:

• patient: The patient id.
• image: The RGB tile of the sample.
• instances: A list of nuclei instances. Each instance represents exactly one nucleus and is in binary format (1 - nucleus, 0 - background)
• tissue: The integer tissue type from which the sample originates, belonging to one of these categories:
  0. Unknown
  1. Breast
  2. Kidney
  3. Liver
  4. Prostate
  5. Bladder
  6. Colon
  7. Stomach

Citation

@article{kumar2017dataset,
    title={A dataset and a technique for generalized nuclear segmentation for computational pathology},
    author={Kumar, Neeraj and Verma, Ruchika and Sharma, Sanuj and Bhargava, Surabhi and Vahadane, Abhishek and Sethi, Amit},
    journal={IEEE transactions on medical imaging},
    volume={36},
    number={7},
    pages={1550--1560},
    year={2017},
    publisher={IEEE}
}

@article{8880654,
  title={A Multi-Organ Nucleus Segmentation Challenge}, 
  author={Kumar, Neeraj and Verma, Ruchika and Anand, Deepak and Zhou, Yanning and Onder, Omer Fahri and Tsougenis, Efstratios and Chen, Hao and Heng, Pheng-Ann and Li, Jiahui and Hu, Zhiqiang and Wang, Yunzhi and Koohbanani, Navid Alemi and Jahanifar, Mostafa and Tajeddin, Neda Zamani and Gooya, Ali and Rajpoot, Nasir and Ren, Xuhua and Zhou, Sihang and Wang, Qian and Shen, Dinggang and Yang, Cheng-Kun and Weng, Chi-Hung and Yu, Wei-Hsiang and Yeh, Chao-Yuan and Yang, Shuang and Xu, Shuoyu and Yeung, Pak Hei and Sun, Peng and Mahbod, Amirreza and Schaefer, Gerald and Ellinger, Isabella and Ecker, Rupert and Smedby, Orjan and Wang, Chunliang and Chidester, Benjamin and Ton, That-Vinh and Tran, Minh-Triet and Ma, Jian and Do, Minh N. and Graham, Simon and Vu, Quoc Dang and Kwak, Jin Tae and Gunda, Akshaykumar and Chunduri, Raviteja and Hu, Corey and Zhou, Xiaoyang and Lotfi, Dariush and Safdari, Reza and Kascenas, Antanas and O’Neil, Alison and Eschweiler, Dennis and Stegmaier, Johannes and Cui, Yanping and Yin, Baocai and Chen, Kailin and Tian, Xinmei and Gruening, Philipp and Barth, Erhardt and Arbel, Elad and Remer, Itay and Ben-Dor, Amir and Sirazitdinova, Ekaterina and Kohl, Matthias and Braunewell, Stefan and Li, Yuexiang and Xie, Xinpeng and Shen, Linlin and Ma, Jun and Baksi, Krishanu Das and Khan, Mohammad Azam and Choo, Jaegul and Colomer, Adrián and Naranjo, Valery and Pei, Linmin and Iftekharuddin, Khan M. and Roy, Kaushiki and Bhattacharjee, Debotosh and Pedraza, Anibal and Bueno, Maria Gloria and Devanathan, Sabarinathan and Radhakrishnan, Saravanan and Koduganty, Praveen and Wu, Zihan and Cai, Guanyu and Liu, Xiaojie and Wang, Yuqin and Sethi, Amit},
  journal={IEEE Transactions on Medical Imaging}, 
  year={2020},
  volume={39},
  number={5},
  pages={1380-1391},
  keywords={Image segmentation;Pathology;Image color analysis;Semantics;Machine learning algorithms;Task analysis;Deep learning;Multi-organ;nucleus segmentation;digital pathology;instance segmentation;aggregated Jaccard index},
  doi={10.1109/TMI.2019.2947628}
}