Automated Identification of Mod-Sev TBI Lesions

Moderate to Severe Traumatic Brain Injury (msTBI) is caused by external forces (eg: traffic accidents, falls, sports) causing the brain to move rapidly within the skull, resulting in complex pathophysiological changes. Multiple primary, secondary, and surgery related processes has the potential to cause structural deformation in the brain. Each patient with msTBI has a unique accumulation of these structural changes, contributing to extremely heterogeneous lesions, considered a hallmark of msTBI (Covington & Duff, 2021). These lesions differ from other common brain pathologies (stroke, MS, brain tumor) in that they can be both focal or diffuse, varying in size, number and laterality, extending through multiple tissue types (GM/WM/CSF), and can also occur in homologous regions of both hemispheres. Lesions such as these can complicate image registration, normalization, and are known to introduce both local and global errors in brain parcellation (Diamond et al., 2020; King et al., 2...

Multi-Class Segmentation of Aortic Branches and Zones in CTA

3D Segmentation of Aortic Branches and Zones on Computed Tomography Angiography (CTA)

Pelvic Bone Fragments with Injuries Segmentation Challenge

Pelvic fractures, typically resulting from high-energy traumas, are among the most severe injuries, characterized by a disability rate over 50% and a mortality rate over 13%, ranking them as the deadliest of all compound fractures. The complexity of pelvic anatomy, along with surrounding soft tissues, makes surgical interventions especially challenging. Recent years have seen a shift towards the use of robotic-assisted closed fracture reduction surgeries, which have shown improved surgical outcomes. Accurate segmentation of pelvic fractures is essential, serving as a critical step in trauma diagnosis and image-guided surgery. In 3D CT scans, fracture segmentation is crucial for fracture typing, pre-operative planning for fracture reduction, and screw fixation planning. For 2D X-ray images, segmentation plays a vital role in transferring the surgical plan to the operating room via registration, a key step for precise surgical navigation.

ToothFairy2: Multi-Structure Segmentation in CBCT Volumes

This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Radboud University Medical Center. The challenge is hosted by grand-challenge and is part of MICCAI2024.

Ischemic Stroke Lesion Segmentation Challenge 2024

Clinical decisions regarding the treatment of ischemic stroke patients depend on the accurate estimation of core (irreversibly damaged tissue) and penumbra (salvageable tissue) volumes (Albers et al. 2018). The clinical standard method for estimating perfusion volumes is deconvolution analysis, consisting of i) estimating perfusion maps through perfusion CT (CTP) deconvolution and ii) thresholding the perfusion maps (Lin et al. 2016). However, the different deconvolution algorithms, their technical implementations, and the variable thresholds used in software packages significantly impact the estimated lesions (Fahmi et al. 2012). Moreover, core tissue tends to expand over time due to irreversible damage of penumbral tissue, with infarct growth rates being patient-specific and dependent on diverse factors such as thrombus location and collateral circulation. Understanding the core's growth rate is clinically crucial for assessing the relevance of transferring a patient to a compre...

AI4Life Microscopy Denoising Challenge

Wellcome to AI4Life-MDC24! In this challenge, we want to focus on an unsupervised denoising of microscopy images. By participating, researchers can contribute to a critical area of scientific research, aiding in interpreting microscopy images and potentially unlocking discoveries in biology and medicine.

AutoPET III

We invite you to participate in the third autoPET Challenge. The focus of this year's challenge is to further refine the automated segmentation of tumor lesions in Positron Emission Tomography/Computed Tomography (PET/CT) scans in a multitracer multicenter setting. Over the past decades, PET/CT has emerged as a pivotal tool in oncological diagnostics, management and treatment planning. In clinical routine, medical experts typically rely on a qualitative analysis of the PET/CT images, although quantitative analysis would enable more precise and individualized tumor characterization and therapeutic decisions. A major barrier to clinical adoption is lesion segmentation, a necessary step for quantitative image analysis. Performed manually, it's tedious, time-consuming and costly. Machine Learning offers the potential for fast and fully automated quantitative analysis of PET/CT images, as previously demonstrated in the first two autoPET challenges. Building upon the insights gai...

The LEOPARD Challenge

Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024) to be able to predict the biochemical recurrence of prostate cancer. Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.

Abdominal Circumference Operator-agnostic UltraSound measurement

Fetal growth restriction (FGR), affecting up to 10% of pregnancies, is a critical factor contributing to perinatal morbidity and mortality (1-3). Strongly linked to stillbirths, FGR can also lead to preterm labor, posing risks to the mother (4,5). This condition often results from an impediment to the fetus' genetic growth potential due to various maternal, fetal, and placental factors (6). Measurements of the fetal abdominal circumference (AC) as seen on prenatal ultrasound are a key aspect of monitoring fetal growth. When smaller than expected, these measurements can be indicative of FGR, a condition linked to approximately 60% of fetal deaths (4). FGR diagnosis relies on repeated measurements of either the fetal abdominal circumference (AC), the expected fetal weight, or both. These measurements must be taken at least twice, with a minimum interval of two weeks between them for a reliable diagnosis (7). Additionally, an AC measurement that falls below the third percentile is, b...

Head and Neck Tumor Segmentation for MR-Guided Applications

This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI

🕹️ 🍄 MARIO : Monitoring AMD progression in OCT

Improve the planning of anti-VEGF treatments

Federated Tumor Segmentation (FeTS) 2024 Challenge

Benchmarking weight aggregation methods for federated training

DREAM olfactory mixtures prediction

Predicting smell from molecule features

precisionFDA Automated Machine Learning (AutoML) App-a-thon

Unlock new insights into its potential applications in healthcare and medicine

ISBI BodyMaps24: 3D Atlas of Human Body

Variations in organ sizes and shapes can indicate a range of medical conditions, from benign anomalies to life-threatening diseases. Precise organ volume measurement is fundamental for effective patient care, but manual organ contouring is extremely time-consuming and exhibits considerable variability among expert radiologists. Artificial Intelligence (AI) holds the promise of improving volume measurement accuracy and reducing manual contouring efforts. We formulate our challenge as a semantic segmentation task, which automatically identifies and delineates the boundary of various anatomical structures essential for numerous downstream applications such as disease diagnosis and treatment planning. Our primary goal is to promote the development of advanced AI algorithms and to benchmark the state of the art in this field. The BodyMaps challenge particularly focuses on assessing and improving the generalizability and efficiency of AI algorithms in medical segmentation across divers...

Cell Tracking Challenge 2024

Develop novel, robust cell segmentation and tracking algorithms

BraTS-ISBI 2024 - Generalizability Across Tumors Challenge

BraTS-GoAT Challenge: Generalizability Across Brain Tumor Segmentation Tasks

Diminished Reality for Emerging Applications in Medicine

Dataset of Synthetic Surgery Scenes: Photorealistic Operating Room Simulations

Harvard Rare Disease Hackathon 2024

Are you a student interested in using AI/ML to tackle rare diseases? Join us!

Light My Cells: Bright Field to Fluorescence Imaging Challenge

Enhance biology and microscopy

Justified Referral in AI Glaucoma Screening

AI-based screening for glaucoma

Project AIR - commercial AI for lung nodule detection on CXR

Lung nodule detection on chest radiographs on a multicenter dataset

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

Bone age prediction on hand radiographs on a multicenter dataset

Biomarkers of Aging Challenge

Systematic evaluation and validation of biomarkers of aging