ANODE09

ANODE09 is an initiative to compare systems that perform automatic detection of pulmonary nodules in chest CT scans on a single common database, with a single evaluation protocol.

LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

ICIAR 2018

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

SLIVER07

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

MoNuSeg

MICCAI 2018 challenge for Multi-organ nuclei segmentation from H&E stained histopathological images.

CHAOS

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

EAD2019

Endoscopic Artefact Detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) Multi-class artefact detection, 2) Region segmentation, 3) Detection generalisation.

ACDC-LungHP

Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology

Decathlon

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

LYON19

Automatic Lymphocyte detection in IHC stained specimens.

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

PAIP 2019

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

VerSe`19

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

StructSeg2019

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

DigestPath2019

Welcome to Digestive-System Pathological Detection and Segmentation 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

PAIP2020

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

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.

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

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.

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

TIGER

Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

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

Surgical Tool Localization in endoscopic videos

Spleen Segmentation

Automatic spleen segmentation on thorax-abdomen CT scans.