PROMISE12

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

CAMELYON17

Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.

CATARACTS

The Challenge on Automatic Tool Annotation for cataRACT Surgery aims at evaluating image-based tool detection algorithms in the context of the most common surgical procedure in the world.

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.

ANHIR

The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.

LYON19

Automatic Lymphocyte detection in IHC stained specimens.

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

curious2019

MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection

Stereo Correspondence and Reconstruction of Endoscopic Data

Stereo Correspondence and Reconstruction of Endoscopic Data

VerSe`19

Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.

VerSe'20

Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.

RibFrac

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

Thyroid Nodule Segmentation and Classification

The main topic of this TN-SCUI2020 challenge is finding automatic algorithms to accurately classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its ground truth class (benign or maglinant) and a detailed delineation of the nodule. This challenge will provide a unique opportunity for participants from different backgrounds (e.g. academia, industry, and government, etc.) to compare their algorithms in an impartial way.

Learn2Reg

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

SARAS-ESAD

This challenge is part of Medical Imaging with Deep Learning conference, 2020. The conference is held between 6 ‑ 8 July 2020 in Montréal. The SARAS (Smart Autonomous Robotic Assistant Surgeon) EU consortium, www.saras-project.eu, is working towards replacing the assistant surgeon in MIS with two assistive robotic arms. To accomplish that, an artificial intelligence based system is required which not only can understand the complete surgical scene but also detect the actions being performed by the main surgeon. This information can later be used infer the response required from the autonomous assistant surgeon.

SurgVisDom

A-AFMA

Prenatal ultrasound (US) measurement of amniotic fluid is an important part of fetal surveillance as it provides a non-invasive way of assessing whether there is oligohydramnios (insufficient amniotic fluid) and polyhydramnios (excess amniotic fluid), which are associated with numerous problems both during pregnancy and after birth. In this Image Analysis Challenge, we aim to attract attention from the image analysis community to work on the problem of automated measurement of the MVP using the predefined ultrasound video clip based on a linear-sweep protocol [1]. We define two tasks. The first task is to automatically detect amniotic fluid and the maternal bladder. The second task is to identify the appropriate points for MVP measurement given the selected frame of the video clip, and calculate the length of the connected line between these points. The data was collected from women in the second trimester of pregnancy, as part of the PURE study at the John Radcliffe Hospital in Oxford, UK.

EndoCV2021

Endoscopy Computer Vision Challenge 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.

CholecTriplet 2021

EndoVis Sub-challenge for Surgical Action Triplet Recognition

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.

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 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

AIROGS

Artificial Intelligence for RObust Glaucoma Screening Challenge

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

CholecTriplet2022: Surgical Action Triplet Detection

Surgical Action Triplet Detection 2022

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 FLARE 2022

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

autoPET

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

ACROBAT 2023

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

SurgT: Surgical Tracking

This challenge consists of surgical videos with a target bounding box and the participants are expected to develop visual tracking algorithms to estimate the trajectory of the bounding box throughout the video-sequence.

Preoperative to Intraoperative Laparoscopy Fusion

Preoperative to Intraoperative Laparoscopy Fusion

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

Surgical Tool Localization in endoscopic videos

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.

MItosis DOmain Generalization Challenge 2022

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.

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.

Surgical Planning in Pediatric Neuroblastoma

Low-dose Computed Tomography Perceptual Image Quality Assessment

Endoscopic surgical tool localization using tool presence labels

The Trauma THOMPSON Challenge

PANORAMA

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

Justified Referral in AI Glaucoma Screening

Abdominal Circumference Operator-agnostic UltraSound measurement

Surgical Visual Understanding

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

Self-supervised learning for 3D light-sheet microscopy image seg