Here is an overview of all challenges that have been organized within the area of medical image analysis that we are aware of. If you know any study that would fit in this overview, or want to advertise your challenge, please leave a message in the forum or send mail to firstname.lastname@example.org and we will add the challenge to the list on this page.
As an endoscopic vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms.
Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?
This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.
The LUMIC challenge tests the accuracy in registration between pre- and post-contrast CT chest images for algorithms, using an anthropomophic digital phantom.
The Continuous Registration Challenge
Submit your method for lung and brain registration on https://github.com/SuperElastix/SuperElastix! Your method is easily accessible to end-users and automatically compiled, tested, and benchmarked weekly on several different data sets.
The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.
This challenge is organised by the French Society on Radiology. After a first phase of collecting medical imaging across France, different challenges will be launch on different organs and different modalities. The results of the challenge will be announced during the French Days of Radiology (12-15 October).
Ischemic Stroke Lesion Segmentation 2018
ISLES 2018 asks for methods that allow the segmentation of stroke lesions based on acute CT perfusion data. A data set of 103 stroke patients and matching expert segmentations are provided.
Brain Tumor Image Segmentation Challenge 2018
BraTS 2018 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’18 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.
Combined Radiology and Pathology Classification
Classify a cohort of lower grade glioma tumor cases into two sub-types: Oligodendroglioma and Astrocytoma when information from two types of imaging data – Radiology images and Pathology images – is used.
Create an algorithm to automate nucleus detection from a variety of brightfield and fluorescence images.
Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.
The goal of the Retinal OCT Fluid Challenge is to compare automated algorithms that are able to detect and segment various types of retinal fluid lesions on a common dataset of optical coherence tomography (OCT) volumes representing different retinal diseases, acquired with devices from different manufacturers.
The Challenge on Automatic Tool Annotation for cataRACT Surgery aims at evaluating image-based tool detection algorithms in the context of the most common surgical procedure in the world.
The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge is brought to you by the EuroPOND consortium in collaboration with the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
This challenge will provide a platform for comparison of various automatic/semi-automatic segmentation algorithms when they are used to delineate organs at risk from CT images for thoracic radiation treatment planning.
Retinal OCT Classification Challenge (ROCC) is organized as a one day Challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with Topcon SD-OCT devices.
Ischemic Stroke Lesion Segmentation 2017
ISLES 2017 asks for methods that predict stroke lesion outcome based on acute MRI data. A multi-spectral data set of 48 stroke patients and matching expert segmentations are provided.
Brain Tumor Image Segmentation Challenge 2017
BraTS 2017 utilizes multi‐institutional pre‐operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthermore, in order to pinpoint the clinical relevance of this segmentation task, BraTS’17 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.
Pediatric Bone Age Challenge
Develop an algorithm which can most accurately determine skeletal age on a validation set of pediatric hand radiographs, using a large data set of over 12,000 images.
Develop an algorithm for MRI cardiac segmentation AND/OR predict pathology of different patients given cardiac MRI images.
Predict the Gleason Grade for a prostate MRI exam consisting of axial and sagittal T2-weighted images, Ktrans images (computed from dynamic contrast-enhanced), and apparent diffusion coefficient (ADC) images.
6-month infant brain MRI segmentation
iSeg-2017 compares (semi-)automatic algorithms for the segmentation of 6-month infant brain tissues and the measurement of corresponding structures using T1- and T2-weighted brain MRI scans.
WMH Segmentation Challenge
This challenge directly compares methods for the automatic segmentation of white matter hyperintensities of presumed vascular origin from FLAIR and T1-weighted MR images.
Cervical Cancer Screening
Develop an algorithm which accurately identifies a woman’s cervix type from images. Doing so will prevent ineffectual treatments and helps referral for cases that require more advanced treatment.
This challenge investigates progress in cerebellum lobule segmentation and labeling, structured around three MRI datasets.
The goal of TraCED is to assess the reproducibility of common and emerging fiber tracking pipelines/algorithms using clinically feasible MR imaging sequences.
Can you improve the detection of lung cancer? Given a CT scan, predict if someone has lung cancer. The 2017 Data Science Bowl has $1 million in prizes.
Tissue Microarray Analysis for Thyroid Cancer
Tissue microarrays (TMAs) can provide new biomarkers that could be of value in diagnosis, predicting outcome and response to therapy. Goal of this challenge is to build predictions models for thyroid cancer from TMAs.
LiTS - Liver Tumor Segmentation
We encourage researchers to develop automatic segmentation algorithms to segment liver lesions in contrast-enhanced abdomen CT scans. The data and segmentations are provided by various clinical sites around the world.
Diagnostic classification of clinically significant prostate lesions using quantitatve image analysis methods.
The goal of this challenge is to evaluate new and existing algorithms for automated detection of cancer metastasis in digitized lymph node tissue sections. Two large datasets from both the Radboud University Medical Center and the University Medical Center Utrecht are provided.
ISBI Endoscopic Image Challenge
The LUNA16 challenge will focus on a large-scale evaluation of automatic nodule detection algorithms for chest CT.
Ischemic Stroke Lesion Segmentation 2016
The ISLES 2016 challenge treats the challenging problem of stroke lesion/clinical outcome prediction from acute MRI scans. They provide acute MRI scans of a large number of acute stroke cases and associated clinical parameters. The associated ground truth is the final lesion volume (Task I) as manually segmented in 3 to 9 month follow-up scans, and the clinical mRM score (Task II) denoting the degree of disability.
Kaggle: Second Annual Data Science Bowl
Declining cardiac function is a key indicator of heart disease. The 2015 Data Science Bowl challenges you to create an algorithm to automatically measure end-systolic and end-diastolic volumes in cardiac MRIs.
Digital Mammography DREAM Challenge
Develop algorithms to classify screening digital mammography images as low and high risk for developing cancer. Thousands of digitized mammograms and outcome data are available for training.
The tasks of the xVertSeg challenge are: To segment vertebrae from the given spine images that include fractured and non-fractured cases. To classify vertebrae from the given spine images into fractured and non-fractured cases along with specific morphological grades and cases of vertebral fractures.
Computational Precision Medicine
The increase in volume and variety of multi modal cancer data along with advances in computational power and analytics have given rise to challenges and opportunities in determining optimal strategies for development of systems for clinical decision support and advancement of precision medicine.
Ultrasound Nerve Segmentation
Identify nerve structures in ultrasound images of the neck. The task in this competition is to segment a collection of nerves called the Brachial Plexus (BP) in ultrasound images.
This challenge is about segmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance images.
BigNeuron is a community effort to define and advance state-of-the-art of single neuron reconstruction: an essential unsolved challenge in brain science.
ImageCLEFmed: Medical Task 2016
ImageCLEFmed 2016 consist of two tasks: analyze compound figures of the biomedical literature and provide a hierarchical classification of body parts in bone x-ray images.
Low Dose CT Grand Challenge
Quantitatively assess the diagnostic performance of denoising and iterative reconstruction techniques on common low-dose patient CT datasets using a detection task.
Ischemic Stroke Lesion Segmentation 2015
ISLES 2015 is a two-task challenge of automatic methods for sub-acute ischemic stroke lesion segmentation and acute stroke penumbra estimation. Versatile multi-center data, two expert segmentations and extensive automatized evaluation.
Gland Segmentation Challenge
Gland morphology is used to assess the degree of malignancy of several adenocarcinomas, including prostate, breast, lung, and colon. This challenge evaluates gland segmentation algorithms on images of H&E stained slides, consisting of a variety of histologic grades.
Brain Tumor Image Segmentation Challenge 2015
Multimodal Brain Tumor Segmentation (BraTS), making available a large dataset of brain tumor MR scans in which the relevant tumor structures have been delineated. Each data set has T1 MRI, T1 contrast-enhanced MRI, T2 MRI, and T2 FLAIR MRI volumes
The VISCERAL Anatomy3 challenge is a continuation and extension of two earlier challenges on whole-body labelling in 3D medical images. Its goal is the segmentation of abdominal organs and the localization of anatomical landmarks.
Lesion Detection Benchmark 2015
Benchmarks lesion detection performance against about 300 expert annotations on anatomical regions such as the bones, liver, brain, lung and lymph nodes in CT and MRI data.
The Colorectal polyp detection challenge compares and evaluates automatic polyp detection systems on 2 databases containing colonoscopy videos and separate colonoscopy video frames.
Evaluates and investigates automated segmentation of gray matter, white matter and cerebrospinal fluid on both the NeoBrainS12 and MRBrainS13 datasets.
Longitudinal Multiple Sclerosis Lesion Segmentation Challenge
Competing teams will apply their automatic lesion segmentation algorithms to MR neuroimaging data acquired at multiple time points from MS patients. Algorithms will be evaluated against manual segmentations from multiple raters in terms of their segmentation accuracy and ability to track lesion evolution.
Cell Tracking Challenge (3d Edition)
Expands on the '13 and '14 benchmark for comparison of cell tracking algorithms by attracting new participants and new submissions. In addition to reusing existing dataset repository, challenging new light-sheet microscopy 3D embryonic developmental data is provided.
Aims to identify the mathematical model for diffusion MRI that best describes the signal from in-vivo Human Brain White Matter acquired on the Connectom scanner with gradients of up to 300mT/m.
Lung nodule classification challenge
Evaluates quantitative image-analysis methods for the diagnostic classification of malignant and benign lung nodules. Training set to be released November 21st.
OPTIMA Retinal Cyst Segmentation Challenge
Brings together and furthers research in the area of retinal cyst segmentation, a key indicator of sight degrading diseases such as RVO and AMD, in SD-OCT ophthalmic imaging. Organised by the OPTIMA lab, Medical University of Vienna.
ISMRM 2015 Tractography Challenge
This challenge evaluates and compares tractography pipelines: provide the best possible reconstruction of fiber pathways in a realistically simulated whole brain diffusion-weighted MR image.
Noninvasive Biomarkers for Liver Fibrosis
Requires only a written proposal describing specific and sensitive biomarker(s) that are highly associated with liver fibrosis and could be used as a surrogate for clinical efficacy and ideally, could guide treatment selection.
LV statical shape modeling: myocardial infarction
Models the statistical shape of the left ventricle (LV) to detect myocardial infarction. training comprises 100 cases with myocardial infarction 100 asymptomatic cases from the Cardiac Atlas Project.
Head and Neck Auto Segmentation
Benchmarks automatic segmentation performance. Provides 25 CT images with manually delineated data of brainstem, mandible, left and right optic nerves, optic chiasm, left and right parotid glands and submandibular glands.
MICCAI Spine Workshop & Challenge
The first challenge concerns vertebral fracture analysis, the second challenge is on intervertebral disc localization and segmentation.
We seek algorithms that perform multi-class classification of patients with Alzheimer’s disease (AD), patients with mild cognitive impairment (MCI) and healthy controls (CN) using multi-center structural MRI data.
MITOS & ATYPIA 14 Contest, hosted by conference ICPR 2014
Detection of mitosis and evaluation of nuclear atypia on breast cancer H&E stained images
Brain Tumor Image Segmentation Challenge 2014
Multimodal Brain Tumor Segmentation (BraTS). It contains three sub-challenges. 1: Segmentation of brain tumor (GBM) and its components, 2: Longitudinal Evaluation of time series image data and 3: Classification into Low- and and High Grade (GBM) gliomas.
Spine and Vertebra Segmentation Challenge
Provides a database of 10 spine CT scans along with manual segmentation of the thoracic and lumbar vertebrae for development, training and testing of spine segmentation algorithms. Five more data sets will be provided for the evaluation phase of the challenge.
VISCERALdetection - Lesion Detection Benchmark 2014
The objective is to detect various lesions in anatomical regions such as the bones, liver, brain, lung, or lymph nodes in CT and MR volumes. There are overall 308 annotated lesions annotated through medical experts.
Cell Tracking Challenge (2nd Edition)
This challenge compares and evaluate state-of-the-art whole-cell and nucleus tracking methods using both real and artificial 2D and 3D time-lapse microscopy videos of fluorescently labeled cells and nuclei moving in realistic environments.
Subthalamic Nucleus Segmentation Challenge 2014
This challenge seeks to bring together leading academic researchers to tackle the difficult problem of Subthalamic Nucleus (STN) segmentation in MRI volumes. Sponsored by Renishaw plc and organised by Aberytswyth University.
Leaf Segmentation Challenge
To advance the state of the art in leaf segmentation and to demonstrate the difficulty of segmenting all leaves in an image of plants, we organize the Leaf Segmentation Challenge (LSC).
The orCaScore challenge compares methods for automatic and semi-automatic coronary artery calcium scoring in cardiac CT scans.
This challenge presents the current state-of-the-art in automated tracking of anatomical landmarks in liver ultrasound images and compare different methods.
DTI Tractography Challenge
This workshop evaluates the performances of tractography algorithms in the reconstruction of peritumoral anatomy and corticospinal tract trajectory on pre-operative and post-operative diffusion data from patients with tumors in or near the motor system.
MICCAI 2014 Machine Learning Challenge
Machine-learning practitioners can try out their favorite (novel or not) supervised learning algorithm(s) on brain imaging (neuroimaging) data for clinically relevant prediction problems.
In this benchmark we will evaluate segmentation and detection algorithms on a large dataset of clinical wide-field-of-view MRI and CT scans. Major organs and substructures have been manually delineated, and anatomical interest points or landmarks have been set.
3D Deconvolution Microscopy
Deconvolution is one of the most common image-reconstruction tasks in 3D fluorescence microscopy. This challenge benchmarks existing deconvolution algorithms and to stimulate the community to look for novel, global and practical approaches to this problem.
Cephalometric X-Ray Landmark Detection
The goal of this challenge is to investigate a suitable automatic landmark detection technology for cephalometric x-ray images and provide a standard evaluation framework with a clinical dataset.
Bone Texture Characterization
The evaluation of osteoporotic disease from bone radiograph images presents a major challenge for pattern recognition and medical applications. The goal is to identify osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis.
Overlapping Cervical Cytology Image Segmentation Challenge
Detection and segmentation of overlapping cells is a major hurdle for robust cervical cell analysis. In this challenge, the targets are to extract the boundaries of individual cytoplasm and nucleus from Pap smear microscopy images.
The Liver CT Annotation Task targets automatic annotation of 3D liver CT data by filling a form of imaging observations at the liver, vessel and lesion levels. The form is built based on a novel open source ontology (ONLIRA) of liver in radiology.
This challenge compares left ventricle segmentation methods for both End Diastolic and End Systolic phase instances in 3D cardiac ultrasound.
CRASS stands for Chest Radiograph Anatomical Structure Segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.
We are presenting results of our segmentation method for subsolid lung nodules.
Brain Tumor Image Segmentation Challenge 2013
Multimodal Brain Tumor Segmentation (BraTS), making available a large dataset of brain tumor MR
scans in which the tumor and edema regions have been manually delineated,
adding another 20 multimodal image volume from high and low grade glioma
patients to the BRATS 2012 data set. All images – in both the publicly
distributed training data set, and the blinded test data set- are annotated
through clinical experts who annotated four different types of tumor substructurs
(edema, enhancing core, non-enhancing core, necrotic core).
Cell Tracking Challenge 2013
Manually tracking cells is an extremely laborious task, due to the large amount of image data acquired during live-cell studies. This challenge compares automatic approaches for cell tracking.
Mitotic activity is one of the strongest prognosticators for invasive breast carcinoma. This challenge evaluates and compares (semi-)automatic mitotic figure detection methods on regions extracted from whole-slide pathology images.
MR Brain Image Segmentation 2013
The MRBrainS evaluation framework compares algorithms for segmentation of grey matter, white matter and cerebrospinal fluid on multi-sequence (T1-weighted, T1-weighted inversion recovery and FLAIR) 3 Tesla MRI scans of the brain.
VISCERAL is an EU funded project in which various benchmarks are organized. The first Benchmark focuses on whole body labelling in 3D medical imaging data.
Automated Segmentation of Prostate Structures (ASPS)
3D Deconvolution Microscopy
The aim of this challenge is to benchmark existing deconvolution algorithms and to stimulate the community to look for novel, global and practical approaches to this problem.
The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.
The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.
Brain Tumor Image Segmentation Challenge 2012
Multimodal Brain Tumor Segmentation (BraTS), making available a large dataset of brain tumor MR scans in which the tumor and edema regions have been manually delineated. In addition, we also provide realistically generated synthetic brain tumor datasets for which the ground truth segmentation is known.
NIBAD'12: Atrophy measurement biomarkers using structural MRI for Alzheimer's disease: a challenge to assess measurement reliability and bias
The aim of the NeoBrainS12 challenge is to compare (semi-)automatic algorithms for segmentation of neonatal brain tissues (cortical and central grey matter, non-myelinated and myelinated white matter, brainstem and cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space) using T1- and T2-weighted MRI scans of the brain.
Mitosis Detection in Breast Cancer Histological Images (MITOS dataset)
Particle tracking challenge 2012
Quantitative analysis of dynamic processes in biological cells requires accurate tracking of large numbers of particles in time-lapse microscopy images. This challenge objectively compares the performance of particle tracking algorithms for this purpose.
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.
Cardiac Motion Analysis Challenge 2011
The objective of this challenge is to evaluate the accuracy and reproducibility of different motion analysis algorithms applied to a multimodal cardiac database. The database includes a dataset from a dynamic phantom1 and 15 datasets from healthy volunteers.
Accurate registration of thoracic CT is both extremely useful in clinical terms and exceptionally challenging due to the elastic nature of lung tissue deformations. EMPIRE10 provides a platform for in-depth evaluation and fair comparison of available registration algorithms for this application.
Knee cartilage segmentation is a clinically relevant segmentation problem which has gained considerable importance in recent years. The goal of SKI10 is to compare different algorithms for cartilage and bone segmentation from knee MRI data.
The goals of this competition were to raise awareness of the problem of automated neuronal reconstruction, spur development of automated and semi-automated algorithms, and to gauge the state of the art in the field.
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.
The goal of the EXACT study is to compare algorithms to extract the airway tree from thoracic computed tomography scans using a common dataset and performance evaluation method.
2009 Prostate Segmentation Challenge
ROC: Retinopathy Online Challenge
Segmentation Validation Engine
A web-based resource that provides automatic evaluation of segmentation results, specifically for the problem of identifying brain versus non-brain in T1-weighted MRI.
VOLCANO'09: Volume Change Analysis of Nodules
The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.
The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.