Diabetic Foot Ulcer Challenge 2020

Diabetic Foot Ulcer Challenge 2020

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.

FeTA - Fetal Tissue Annotation Challenge

Fetal Tissue Annotation Challenge

DFUC2021

Diabetic Foot Ulcer Challenge 2021

INSTANCE2022

The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)

DFUC 2022

Diabetes is a global epidemic affecting around 425 million people and expected to rise to 629 million by 2045. Diabetic Foot Ulcer (DFU) is a severe condition that can result from the disease. The rise of the condition over the last decades is a challenge for healthcare systems. Cases of DFU usually lead to severe conditions that greatly prolongs treatment and result in limb amputation or death. Recent research focuses on creating detection algorithms to monitor their condition to improve patient care and reduce strain on healthcare systems. Work between Manchester Metropolitan University, Lancashire Teaching Hospitals and Manchester University NHS Foundation Trust has created an international repository of up to 11,000 DFU images. Analysis of ulcer regions is a key for DFU management. Delineation of ulcers is time-consuming. With effort from the lead scientists of the UK, US, India and New Zealand, this challenge promotes novel work in DFU segmentation and promote interdisciplinary researcher collaboration.

Diabetic Retinopathy Analysis Challenge MICCAI2022

Diabetic Retinopathy (DR) lesions segmentation, image quality assessment and classification of proliferatived DR (PDR) and non-PDR in ultra-wide optical coherence tomography angiography mosaic (UW-OCTA-M) images

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.

Universal Lesion Segmentation Challenge '23