RadboudCOVID

Data from RadboudUMC from Covid-19 (suspected) subjects

coronacases.org

10 CT scans from the website https://coronacases.org/

LOLA11

55 scans from the LOLA11 challenge

LUNA16

888 CT scans from the LUNA16 challenge

Patch Camelyon

Indicate which patches are malignant.

CORADS Score Exam

Assign a CORADS score to 25 cases. You will receive the results of the test by e-mail.

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

Adhesion cine-MRI tutorial

A short 4-case tutorial for adhesion detection on abdominal cine-MRI

VESSEL12

The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.

CRASS

CRASS stands for Chest Radiograph Anatomical Structure Segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.

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.

CAUSE07

The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.

LOLA11

The goal of LOLA11 (LObe and Lung Analysis 2011) is to compare methods for (semi-)automatic segmentation of the lungs and lobes from chest computed tomography scans. Any team, whether from academia or industry, can join.

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

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.

DRIVE

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

HC18

Automated measurement of fetal head circumference using 2D ultrasound images

iChallenge-AMD

Age-related Macular Degeneration Challenge

ODIR-2019

北京大学国际眼底图像智能识别竞赛 Peking University International Competition on Ocular Disease Intelligent Recognition

Lymphocyte Assessment Hackathon

Lymphocyte Assessment Hackathon in conjunction with the MICCAI COMPAY 2019 Workshop on Computational Pathology

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)

Learn2Reg

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

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.

Where is VALDO?

Vascular Lesion Detection Challenge at MICCAI 2021

Carotid Artery Vessel Wall Segmentation Challenge

To segment the vessel wall of the carotid artery on black-blood MRI images

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.

KiTS21

The 2021 MICCAI Kidney and Kidney Tumor Segmentation challenge

BrainPTM 2021

Brain Pre-surgical Tractography Mapping (BrainPTM) in real clinical scans.

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

FeTA - Fetal Tissue Annotation Challenge

Fetal Tissue Annotation Challenge

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.

NODE21

NODE21: generate and detect nodules on chest radiographs

SKI10

The goal of SKI10 was to develop and compare algorithms for cartilage and bone segmentation from knee MRI data.

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

TIGER

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

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

EndoCV2022

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

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.

INSTANCE2022

The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)

BCNB

Early Breast Cancer Core-Needle Biopsy WSI Dataset

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

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.

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

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.

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.

ACMMM MEGC2022: Facial Micro-Expression Grand Challenge

Spotting Facial Macro- and Micro-Expressions in Long Videos

Project AIR - commercial AI for bone age prediction on hand XR

Head-to-head performance evaluation of commercially available AI products. This challenge shows the results for bone age prediction on hand radiographs on a multicenter dataset (seven centers) from the Netherlands.

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.

SPIDER

Universal Lesion Segmentation Challenge '23

Spleen Segmentation

Automatic spleen segmentation on thorax-abdomen CT scans.

Vertebra segmentation and labeling

Segmentation and labeling of the vertebrae in CT scans with arbitrary field of view.

Tumor Detection in Lymph Nodes

Tissue-Background Segmentation

CXR Cardiomegaly Detection

Detect cardiomegaly on chest radiographs through the segmentation of the heart and lungs .

Gleason Grading of Prostate Biopsies

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

Gleason Grading of Prostate Biopsies (non-normalized)

Automated Gleason grading of prostate biopsies following the Gleason Grade Group system. This version of the algorithm runs without data normalization.

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

HookNet-Breast

Segmentation algorithm for histopathology breast tissue.

Lung Cancer Segmentation

Lung cancer segmentation in H&E stained histopathological images.

Fluid Segmentation in Retinal Optical Coherence Tomography (OCT)

Segments intraretinal fluid, subretinal fluid, and pigment epithelial detachments in Optical Coherence Tomography scans. Optimized for Spectralis, Cirrus and Topcon scanners.

HookNet-Lung

Segmentation algorithm for histopathology lung tissue.

Femur segmentation in CT

Segments the left and right femur in CT images

Neural Image Compression

Compresses whole slide images into much smaller volumes

CXR Total Lung Volume Measurement

Measurement of total lung volume from chest radiographs using frontal and lateral radiographs

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.

Quality assessment of whole-slide images through artifact detection

Quality scoring with artifact detection in whole slide images; out-of-focus, tissue folds, ink, dust, pen mark, and air bubbles.

Deep-Learning-Based CT Lung Registration

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

Whole-heart segmentation in non-contrast-enhanced CT

Algorithm for the automatic segmentation of cardiac structures in non-contrast-enhanced CT images. The structures to be segmented are the left ventricular myocardium, left ventricular cavity, right ventricle, left atrium, right atrium, ascending aorta, and the pulmonary artery trunk until the first bifurcation. The underlying publication can be found under https://doi.org/10.1002/mp.14451

corrField

Correspondence fields for large motion image registration

Colon Tissue segmentation

Tissue segmentation network for colon histopathology images

Body composition

Locates L3 in a CT scan and measures the area of subcutaneous, visceral, and intermuscular adipose tissue and smooth muscle. The average Hounsfield Units of each area are also computed.

Nuclear Pleomorphism Scoring

Scoring nuclear pleomorphism grade in whole-slide breast histopathology images

Hip segmentation in CT

Segments the left and right hip bones in CT images

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

STOIC2021 baseline

Example algorithm for the STOIC2021 COVID-19 AI Challenge

Breast Cancer Segmentation and Scoring in H&E

Liver segmentation

Segments the liver and liver tumors using nnUNet

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

Airway Anatomical Labeling

Given an airway segmentation where individual airway branches are extracted, this algorithm will automatically find 18 segmental branches, including 8 from the left lung (LB1+2, LB3, LB4, LB5, LB6, LB7+8, LB9, and LB10) and 10 from the right lung (RB1-10).

Pancreatic Ductal Adenocarcinoma Detection in CT

airogs_ur

Submission for AIROGS challenge

Airogs_fine3

base_airgos_update

cellular composition

Wrist segmentation

Segments the carpal bones and radius and ulna in (dynamic) CT scans of the wrist.

Tibia segmentation in CT

Segments the left and right tibia in CT images

Rib segmentation

Segments and labels the ribs in CT images

Endometrial Carcinoma classification

CLAM model that computes the probability of the WSI being (pre)malignant and also outputs an interpretable heatmap.

Colon Budding in IHC

Automatic tumor bud detection in IHC stained slides of CRC

SPIDER Baseline nnU-Net

Carpal instability measurements

Automated AI pipeline for conducting carpal instability measurements on conventional radiographs of the hand and wrist.

SPIDER Baseline IIS