RadboudCOVID

Data from RadboudUMC from Covid-19 (suspected) subjects

LUNA16

888 CT scans from the LUNA16 challenge

KiTS21 Sanity Check

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

CORADS Score Practice

Practice CORADS scoring with 50 cases. You get instant feedback after every case.

Demonstrator of SATORI Lung Analysis with integrated image quality analysis

Demonstration of lung analysis in SATORI with image quality analysis and integration of CORADS and severy score

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

DRIVE

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

CHAOS

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

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.

CT diagnosis of COVID-19

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective Deep Learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It’s a binary classification problem based on CT images.

RIADD (ISBI-2021)

Retinal Image Analysis for multi-Disease Detection

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.

KiTS21

The 2021 MICCAI Kidney and Kidney Tumor Segmentation challenge

NODE21

NODE21: generate and detect nodules on chest radiographs

TIGER

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

MELA2022

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

Parse2022

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.

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.

MICCAI FLARE 2022

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

AGGC22

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

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

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.

Universal Lesion Segmentation Challenge '23

Lung cancer risk estimation on thorax CT scans - DSB2017 grt123

Automatic lung cancer risk estimation from thoracic CT scans

Gleason Grading of Prostate Biopsies

Automated Gleason grading of prostate biopsies following the Gleason Grade Group system.

Pulmonary Lobe Segmentation

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

Scaphoid fracture detection

Automatic detection of scaphoid fractures on hand, wrist, and scaphoid x-rays.

CORADS-AI

Segments pulmonary lobes and lesions and computes the CORADS and CT Severity Score from a non-contrast CT scan.

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

HookNet-Lung

Segmentation algorithm for histopathology lung tissue.

Femur segmentation in CT

Segments the left and right femur in CT images

Calcium scoring in non-contrast CT showing the heart

Automatic quantification of calcifications in the three main coronary arteries (LAD, LCX, RCA) and the thoracic aorta in non-contrast CT scans.

Deep-Learning-Based CT Lung Registration

Registration of inspiration and expiration lung CT scans using a neural network trained with multiple anatomical constraints.

corrField

Correspondence fields for large motion image registration

Vertebral Fracture Assessment

A neural network that assesses vertebral fractures according to the Genant classification

Vertebral Abnormality Scoring

Score from 0 to 100 that expresses how abnormal the shape of a vertebra is

Lobe-Wise Lung Function Estimation from CT

Produces patient-level and lobe-level estimates of DLCO and of FEV1 and FVC pre- and post-bronchodilator

Multi-view scaphoid fracture detection

Automated scaphoid fracture detection on conventional radiographs of the hand, wrist, and scaphoid in any view.

Lung nodule detection for routine clinical CT scans

Deep learning for the detection of pulmonary nodules in chest CT scans

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

Balaitous

A deep learning model to estimate COVID disease and severity from a CT scan

Pancreatic Ductal Adenocarcinoma Detection in CT

Airogs_fine3

PI-CAI: Baseline nnU-Net (semi-supervised)

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

Deep learning to estimate pulmonary nodule malignancy risk using a current and a prior CT image

Deep learning to estimate pulmonary nodule malignancy risk using a prior CT image

Universal Lesion Segmentation [ULS23 Baseline]

Univeral Lesion Segmentation algorithm for Computed Tomography