Decathlon

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

QUBIQ

Quantification of Uncertainties in Biomedical Image Segmentation Challenge

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

EndoCV2022

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

SynthRAD2023

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

MICCAI HECKTOR 2022

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

Fully Automated Radiotherapy Treatment Planning Challenge

Evaluating fully automated external beam radiotherapy treatment planning in prostate cancer

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

A semantic multimodal segmentation challenge comprising 30 organs at risk

SEG.A. - Segmentation of the Aorta

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

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

AutoPET III

Multi-Class Segmentation of Aortic Branches and Zones in CTA

3D Segmentation of Aortic Branches and Zones on Computed Tomography Angiography (CTA)

SynthRAD2025

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

Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation

TrackRad: Real-time tumor tracking for MRI-guided radiotherapy

HEad and neCK TumOR Lesion Segmentation, Diagnosis and Prognosis

HECKTOR 2025 is the next iteration of a medical imaging challenge focused on improving automated analysis of head and neck cancer using multimodal PET/CT data. The challenge features three complementary tasks that span the clinical workflow: automatic detection and segmentation of primary tumors and lymph nodes, prediction of recurrence-free survival using imaging and clinical data, and diagnosis of HPV status, which is crucial for treatment decisions. The 2025 edition significantly expands on previous challenges with a larger dataset exceeding, refined evaluation metrics that better assess both detection and segmentation capabilities, and the addition of radiotherapy planning dose maps as an information channel. This challenge aims to advance the development of clinical tools that can aid in treatment planning, outcome prediction, and diagnosis in head and neck cancer patients, ultimately supporting more personalized patient management approaches.