PROMISE12

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

MICCAI FLARE 2022

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

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

ARCADE-MICCAI2023

Universal Lesion Segmentation Challenge '23

Pulmonary Lobe Segmentation

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

CORADS-AI

Segments pulmonary lobes and lesions and computes the CORADS and CT Severity Score from a non-contrast CT scan.

Lung nodule detection for routine clinical CT scans

Deep learning for the detection of pulmonary nodules in chest CT scans

Universal Lesion Segmentation [ULS23 Baseline]

Univeral Lesion Segmentation algorithm for Computed Tomography