VESSEL12

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

CAUSE07

The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.

LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

SLIVER07

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

IDRiD

This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.

DRIVE

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

REFUGE

The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.

PROSTATEx

Classification of clinical significance of prostate lesions using multi-parametric MRI data

CHAOS

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

iChallenges

We organized a serial of challenges on different eye image modalities, such as REFUGE, PALM, RETOUCH, etc.

Decathlon

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

iChallenge-AMD

Age-related Macular Degeneration Challenge

RibFrac

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

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.

Cross-Modality Domain Adaptation Image Segmentation - 2021

The CrossMoDA challenge 2021 introduces the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

BrainPTM 2021

Brain Pre-surgical Tractography Mapping (BrainPTM) in real clinical scans.

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

FeTA - Fetal Tissue Annotation Challenge

Fetal Tissue Annotation Challenge

QUBIQ2021

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

STOIC2021 - COVID-19 AI Challenge

COVID-19 Artificial Intelligence Challenge: automated diagnosis, and prognostic evaluation based on computed tomography

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

KiPA22 (Regular Challenge)

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

INSTANCE2022

The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)

ATLAS R2.0 - Stroke Lesion Segmentation

Anatomical Tracings of Lesions After Stroke

MICCAI FLARE 2022

MICCAI 2022 Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation (FLARE) Challenge

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

Cross-Modality Domain Adaptation: Segmentation & Classification

The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

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.

Ischemic Stroke Lesion Segmentation Challenge

PAIP 2023: TC prediction in pancreatic and colon cancer

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

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

LNQ2023

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.

ARCADE-MICCAI2023

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.

Universal Lesion Segmentation Challenge '23

AutoPET III