VESSEL12

The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.

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.

Decathlon

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

Automated Segmentation Of Coronary Arteries

Automated Segmentation Of Coronary Arteries

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.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

Foot Ulcer Segmentation Challenge

CholecTriplet 2021

EndoVis Sub-challenge for Surgical Action Triplet Recognition

PAIP2021

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

FeTA - Fetal Tissue Annotation Challenge

Fetal Tissue Annotation Challenge

DFUC2021

Diabetic Foot Ulcer Challenge 2021

SARAS-MESAD

This challenge is organized under MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. The event will be held from September 27th to October 1st 2021 in Strasbourg, France. The challenge focuses on multi-domain surgeon action detection.

CholecTriplet2022: Surgical Action Triplet Detection

Surgical Action Triplet Detection 2022

EndoCV2022

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

Parse2022

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.

TDSC-ABUS2023

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound

Surgical Tool Localization in endoscopic videos

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.

Surgical Visual Understanding