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.

LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

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

StructSeg2019

Welcome to Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

LNDb Challenge

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

RibFrac

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

Learn2Reg

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

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.

SynthRAD2023

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

autoPET

Automatic lesion segmentation in whole-body FDG-PET/CT

MICCAI HECKTOR 2022

Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge

A semantic multimodal segmentation challenge comprising 30 organs at risk

autoPET-II

Automated Lesion Segmentation in PET/CT - Domain Generalization

SPIDER

SegRap 2023

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).

Head and Neck Tumor Segmentation for MR-Guided Applications

This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI

Universal Lesion Segmentation Challenge '23

AutoPET III

Pulmonary Lobe Segmentation

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

Pulmonary Nodule Malignancy Prediction

Deep Learning for Malignancy Risk Estimation of Low-Dose Screening CT Detected Pulmonary Nodules