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 send an email to support@grand-challenge.org and we
will add the challenge to the list on this page.
The Pathologic Myopia Challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients.
Endoscopic Artefact Detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) Multi-class artefact detection, 2) Region segmentation, 3) Detection generalisation.
The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.
Predicting fluid intelligence from T1-weighed MRI (11K subjects in total, age 9-10 years). The data of about 4k individuals will be provided for training.
Develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.
A dataset of cells with labels (normal versus malignant) will be provided to train machine learning based classifier to identify normal cells from leukemic blasts (malignant cells).
The Cell Tracking Challenge (CTC) was launched in 2012 to objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods. We are launching now a new time-lapse cell segmentation benchmark on the same ...
The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.
Peripheral Artery:Vein Enhanced Segmentation (PAVES) is the challenge focussed on providing easily interpretable and relevant images that can be readily understood by clinicians (vascular interventional radiologists & vascular surgeons) from MRA datasets where the venous ...
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.
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 LUMIC challenge tests the accuracy in registration between pre- and post-contrast CT chest images for algorithms, using an anthropomophic digital phantom.
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 ...
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 ...
fastMRI is collaborative research project from Facebook AI Research (FAIR) and NYU Langone Health to investigate the use of AI to make MRI scans up to 10 times faster. Details coming soon.
MURA (musculoskeletal radiographs) is a large dataset of bone X-rays. Algorithms are tasked with determining whether an X-ray study is normal or abnormal.
Assess the probability of a TB patient having resistant form of tuberculosis based on the analysis of chest CT scan, automatically categorize each TB case into one of the following five types: (1) Infiltrative, (2) ...
The aim of the challenge is the systematic evaluation of the performance of algorithms that enable the harmonisation of muti-shell DW MRI data from clinically available Siemens Prisma Scanner and the state-of-the-art Siemens Connectome Scanner. ...
The goal of this recurring challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images. There are three separate tasks: lesion segmentation, lesion attribute detection and ...
The goal of MICCAI 2018 Challenge on Automatic IVD Localization and Segmentation from 3D Multi-modality MR (M3) Images is to investigate (semi-)automatic IVD localization and segmentation algorithms and provide a standard evaluation framework with a ...
The purpose of this challenge is to directly compare methods for segmentation of gray matter, white matter, cerebrospinal fluid, and other structures on 3T MRI scans of the brain, and to assess the effect of ...
Contestants are tasked to predict, using primary tumor 18F-FDG PET-derived radiomics features +/- matched clinical data, whether a tumor arising from the oropharynx will be controlled by definitive radiation treatment (RT).
This challenge addresses the validation of 3D diffusion MRI fiber tractography. The task is to provide the most accurate reconstruction of fiber pathways in both a physical phantom and real brain tissue.
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.
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.
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 ...
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 ...
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).
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.
Retinal OCT Fluid Challenge (RETOUCH) compares automated algorithms that are able to detect and segment different types of retinal fluid in optical coherence tomography (OCT).
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.
Assess the probability of a TB patient having resistant form of tuberculosis based on the analysis of chest CT scan, and automatically categorize each TB case into one of the following five types: Infiltrative, Focal, ...
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.
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.
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 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.
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.
This challenge directly compares methods for the automatic segmentation of white matter hyperintensities of presumed vascular origin from FLAIR and T1-weighted MR images.
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.
CoronARe compares state-of-the-art methods in terms of 3D reconstruction accuracy of coronary arteries from interventional C-arm rotational angiography.
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.
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 ...
The goal of TraCED is to assess the reproducibility of common and emerging fiber tracking pipelines/algorithms using clinically feasible MR imaging sequences.
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 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 ...
To develop a surgical workflow monitoring system by detecting both the surgical workflow and identifying all surgical tools that are present in an image.
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 ...
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.
The goal of this challenge is to evaluate algorithms for automatic reconstruction of neurons and neuronal connectivity from serial section electron microscopy data.
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.
This workshop brings together two MICCAI-Workshop series (CVII and STENT) in order to form a common event on technological and scientific research concerned with endovascular procedures.
Its aim is to create a common ground to compare methods to find predictive MRI features, which help to characterise and distinguish mild traumatic brain injury patients from each other and healthy subjects.
Quantitatively assess the diagnostic performance of denoising and iterative reconstruction techniques on common low-dose patient CT datasets using a detection task.
BigNeuron is a community effort to define and advance state-of-the-art of single neuron reconstruction: an essential unsolved challenge in brain science.
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.
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.
Multimodal Brain Tumor Segmentation (BraTS), making available a large dataset of brain tumor MR scans in which the relevant tumor structures have been delineated.
Evaluates multi-atlas segmentation algorithms on a newly created and released standardized dataset of abdominal and pelvic anatomy on clinically acquired CT
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.
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.
Evaluates quantitative image-analysis methods for the diagnostic classification of malignant and benign lung nodules. Training set to be released November 21st.
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.
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.
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.
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 ...
Evaluates and investigates automated segmentation of gray matter, white matter and cerebrospinal fluid on both the NeoBrainS12 and MRBrainS13 datasets.
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, ...
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 ...
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.
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.
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 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 ...
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.
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 ...
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.
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.
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.
The Shape Symposium hosts the first Grand Challenge in Statistical Shape Modeling. The task is to build a model of the liver from a set of segmented training datasets.
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.
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.
This challenge presents the current state-of-the-art in automated tracking of anatomical landmarks in liver ultrasound images and compare different methods.
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 ...
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.
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 ...
CRASS stands for Chest Radiograph Anatomical Structure Segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.
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.
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.
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.
Multimodal Brain Tumor Segmentation (BraTS), making available a large dataset of brain tumor MRscans in which the tumor and edema regions have been manually delineated,adding another 20 multimodal image volume from high and low grade ...
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.
This is a segmentation challenge for automatically segmenting anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images.
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.
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 ...
In this challenge, a full stack of EM slices will be used to train machine learning algorithms for the purpose of automatic segmentation of neural structures.
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 ...
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.
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 ...
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 ...
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.
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.
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.
