KiTS21 Sanity Check

The three "sanity check" cases for the KiTS21 submission period

LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

CHAOS

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

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

StructSeg2019

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

RibFrac

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

KiTS21

The 2021 MICCAI Kidney and Kidney Tumor Segmentation challenge

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

MELA2022

MICCAI 2022 MELA Challenge: A Large-Scale Detection Benchmark of 1,100 CT Scans for Mediastinal Lesion Analysis

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

autoPET

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

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

Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

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

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

ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge

This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.

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

DENTEX - MICCAI23

Dental Enumeration and Diagnosis on Panoramic X- rays Challenge

Endoscopic surgical tool localization using tool presence labels

Universal Lesion Segmentation Challenge '23

Lung cancer risk estimation on thorax CT scans - DSB2017 grt123

Automatic lung cancer risk estimation from thoracic CT scans

Pulmonary Lobe Segmentation

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

Pulmonary Nodule Malignancy Prediction

Deep Learning for Malignancy Risk Estimation of Low-Dose Screening CT Detected Pulmonary Nodules

Lung cancer risk estimation on thorax CT scans - DSB2017 JulianDaniel

Automatic lung cancer risk estimation from thoracic CT scans

Clinically Significant Prostate Cancer Detection in bpMRI using models trained with Report Guided Annotations

PI-CAI: Baseline U-Net (supervised)

PI-CAI: Baseline nnU-Net (supervised)

Baseline algorithm submission for PI-CAI based on the nnU-Net framework

PI-CAI: Baseline nnDetection (semi-supervised)

Baseline semi-supervised algorithm submission for PI-CAI based on the nnDetection framework