Welcome to Digestive-System Pathological Detection and Segmentation Challenge 2019.
Release of training data: Jun. 14th, 2019 Submission start time: Sep. 16th, 2019 11:59 UTC-8 Beijing Time Submission start time extends to: Sep. 19th, 2019 11:59 UTC-8 Beijing Time Submission deadline: Sep. 23th, 2019 11:59 UTC-8 Beijing Time Submission deadline extends to: Sep. 26th, 2019 11:59 UTC-8 Beijing Time Announcement of results: Oct. 1st, 2019
- results site (under repair): http://www.digestpath-challenge.org/
Presentation and award ceremony: Oct. 13th, 2019 Presentation and award ceremony extends to: Oct. 17th, 2019
- Public submission: Dec. 26th, 2019 13:00 UTC-8 Beijing Time
The goal of the challenge is to set up tasks for evaluating automatic algorithms on signet ring cell detection and colonoscopy tissue screening from digestive system pathological images. This will be the first challenge and first public dataset on signet ring cell detection and colonoscopy tissue screening. Releasing the large quantity of expert-level annotations on digestive-system pathological images will substantially advance the research on automatic pathological object detection and lesion segmentation.
- Task 1: Signet ring cell detection.
- Task 2: Colonoscopy tissue segmentation and classification.
Top-ranking teams on each task will be invited to submit full papers on describing their methods to the special issue on Deep learning for Medical Image Computing of Neurocomputing (IF= 4.072). The guest editors are:
Hongsheng Li (SenseTime Research & CUHK)
Shaoting Zhang (SenseTime Research)
Dimitris N. Metaxas (Rutgers University)
Examination of pathological images is the golden standard for diagnosing and screening cancers in the digestive system. Digital pathology has become increasingly popular in recent years and allows examination of high-resolution whole image (WSI) in remote locations. Such a functionality is convenient for hospitals in rural areas of developing countries, where there are not enough experienced pathologists for accurate diagnosis. However, manual analysis of WSI is still a time-consuming task for the pathologists because the WSI can be up to size 100,000 X 100,000 pixels. This prevents remote examination to be widely adopted in developing countries/regions where there are not enough pathologists. One promising solution is to develop medical models to automatically detect, segment, and classify cells of interests in different pathological images. Such problems attract much attention from the medical imaging community and there is a larger number of existing papers on pathological image segmentation.
In the challenge, participants will be provided with 2 datasets:
- Signet ring cell dataset
A total of 155 patients' 687 pathological images are used in this challenge. The training set is released with 99 patients' 460 images, in which 77 images from 20 patients contain ring cells annotation. For method evaluation, another 56 patients' 227 images are utilized, in which 27 images from 11 patients contain ring cells. The images are from 2 organs, including gastric mucosa and intestine.
- Colonoscopy tissue segment dataset
A total of 476 patients' 872 tissue slices of an average size of 3000x3000 are used in this challenge. The training set is released with 324 patients' 660 images, in which 250 images from 93 patients contain lesion annotation. We will also provide another 152 patients' 212 tissues as the testing set, in which 90 images from 65 patients contain lesion.
The data and annotation are provided by Histo Pathology Diagnostic Center together with cooperated hospitals.
Da Q, Huang X, Li Z, et al. DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System[J]. Medical Image Analysis, 2022: 102485。（https://doi.org/10.1016/j.media.2022.102485）
Li, J., Yang, S., Huang, X., Da, Q., Yang, X., Hu, Z., ... & Li, H. (2019, June). Signet Ring Cell Detection with a Semi-supervised Learning Framework. In International Conference on Information Processing in Medical Imaging (pp. 842-854). Springer, Cham. (https://arxiv.org/abs/1907.03954)
If you have any questions or comments, please mail to email@example.com or firstname.lastname@example.org.
This challenge is origanized by the following institutions: