SLIVER07

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

Where is VALDO?

Vascular Lesion Detection Challenge at MICCAI 2021

BrainPTM 2021

Brain Pre-surgical Tractography Mapping (BrainPTM) in real clinical scans.

ATLAS R2.0 - Stroke Lesion Segmentation

Anatomical Tracings of Lesions After Stroke

MICCAI FLARE 2022

MICCAI 2022 Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation (FLARE) Challenge

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

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

Cross-Modality Domain Adaptation: Segmentation & Classification

The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

Ischemic Stroke Lesion Segmentation Challenge

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.

Universal Lesion Segmentation Challenge '23

Automated Identification of Mod-Sev TBI Lesions