VESSEL12

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

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.

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.

SLIVER07

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

Decathlon

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

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

LNDb Challenge

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

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)

QUBIQ

Quantification of Uncertainties in Biomedical Image Segmentation Challenge

RIADD (ISBI-2021)

Retinal Image Analysis for multi-Disease Detection

MitoEM

Large-scale 3D mitochondria instance segmentation benchmark

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.

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

SegPC-2021

This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

Foot Ulcer Segmentation Challenge

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

RealNoiseMRI

Brain MRI reconstruction challenge with realistic noise

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

NuCLS

Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].

Breast Cancer Segmentation

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

FeTA - Fetal Tissue Annotation Challenge

Fetal Tissue Annotation Challenge

NODE21

NODE21: generate and detect nodules on chest radiographs

WSSS4LUAD

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

QUBIQ2021

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 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

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

KiPA22 (Regular Challenge)

The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.

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.

TDSC-ABUS2023

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound

ATLAS R2.0 - Stroke Lesion Segmentation

Anatomical Tracings of Lesions After Stroke

SynthRAD2023

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

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

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.

K2S: from undersampled K-space To automatic Segmentation

K2S: from undersampled K-space To automatic Segmentation

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.

MItosis DOmain Generalization Challenge 2022

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

Xray Projectomic Reconstruction Extracting Segment with Skeleton

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.

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.

ARCADE-MICCAI2023

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

SegRap 2023

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).

CL-Detection 2023

Cephalometric landmark detection in lateral x-ray images

OCELOT 2023: Cell Detection from Cell-Tissue Interaction

Universal Lesion Segmentation Challenge '23

AutoPET III

Ischemic Stroke Lesion Segmentation Challenge 2024

TopCoW 2024 Challenge

Segment, classify, and detect the Circle of Willis (CoW) for both CTA and MRA

Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation

Calibration and Uncertainty for multiRater Volume Assessment in

Balaitous

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