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

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.

PALM

The Pathologic Myopia Challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients.

PAIP 2019

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

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)

Where is VALDO?

Vascular Lesion Detection Challenge at MICCAI 2021

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.

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

3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022

Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.

autoPET

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

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.

autoPET-II

Automated Lesion Segmentation in PET/CT - Domain Generalization

Universal Lesion Segmentation Challenge '23

AutoPET III

Ischemic Stroke Lesion Segmentation Challenge 2024

Pelvic Bone Fragments with Injuries Segmentation Challenge

Pelvic fracture segmentation in CT and X-ray

Challenge Hosting Masterclass 2025

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.

Deep-learning Evaluation for Enhanced Prognostics - PSMA PET

PANTHER Challenge

autoPET/CT IV

Automated Lesion Segmentation in PET/CT - The human frontier

Automated Identification of Mod-Sev TBI Lesions 2025

ToothFairy3: Multi-Class Segmentation in CBCT Volumes

ToothFairy3, part of the ODIN2025 challenge cluster at MICCAI2025, advances CBCT segmentation with an expanded 77-class dataset and a new emphasis on computational efficiency. It introduces two tasks: a runtime-aware multi-structure segmentation and a novel interactive track for Inferior Alveolar Canal (IAC) segmentation using minimal user input. The challenge supports the development of both automated and prompt-based interactive AI tools to enhance clinical workflows in dentistry and maxillofacial surgery.