LOLA11

The goal of LOLA11 (LObe and Lung Analysis 2011) is to compare methods for (semi-)automatic segmentation of the lungs and lobes from chest computed tomography scans. Any team, whether from academia or industry, can join.

DRIVE

Develop a system to automatically segment vessels in human retina fundus images.

PatchCamelyon

PatchCamelyon is a new and challenging image classification dataset of 327.680 color images (96 x 96px) extracted from histopathology images of the CAMELYON16 challenge. The goal is to detect breast cancer metastasis in lymph nodes.

PAIP2020

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

Diabetic Foot Ulcer Challenge 2020

Diabetic Foot Ulcer Challenge 2020

COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020

This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

Breast Cancer Segmentation

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

NODE21

NODE21: generate and detect nodules on chest radiographs

WSSS4LUAD

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

KiPA22 (Regular Challenge)

The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

Universal Lesion Segmentation Challenge '23