PROMISE12

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

ANHIR

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.

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

BCNB

Early Breast Cancer Core-Needle Biopsy WSI Dataset

Breast Cancer Immunohistochemical Image Generation Challenge

The Breast Cancer Immunohistochemical Image Generation Challenge aims to directly generate IHC-stained breast cancer histopathology images from HE-stained images.

The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge

A semantic multimodal segmentation challenge comprising 30 organs at risk

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

Foundation Model Prompting for Medical Image Classification

The primary objective of this challenge is to promote the development and evaluation of model adaptation techniques for medical image classification to leverage the existing foundation models.

SegRap 2023

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

Hypoxic Ischemic Encephalopathy Lesion Segmentation Challenge

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

Universal Lesion Segmentation Challenge '23

Justified Referral in AI Glaucoma Screening

Light My Cells : Bright Field to Fluorescence Imaging Challenge

Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! 🌐🔬 #LightMyCellsChallenge

Diminished Reality for Emerging Applications in Medicine

The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory. DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity. The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded view of the operative site.

AutoPET III

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.

Pelvic Bone Fragments with Injuries Segmentation Challenge

Multi-Class Segmentation of Aortic Branches and Zones in CTA

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