DRIVE

Develop a system to automatically segment vessels in human retina fundus images.

LNDb Challenge

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

RIADD (ISBI-2021)

Retinal Image Analysis for multi-Disease Detection

EndoCV2021

Endoscopy Computer Vision Challenge 2021

Carotid Artery Vessel Wall Segmentation Challenge

To segment the vessel wall of the carotid artery on black-blood MRI images

FLARE21

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

NODE21

NODE21: generate and detect nodules on chest radiographs

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

EndoCV2022

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

Parse2022

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.

Preoperative to Intraoperative Laparoscopy Fusion

Preoperative to Intraoperative Laparoscopy Fusion

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-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.

Ischemic Stroke Lesion Segmentation Challenge

PAIP 2023: TC prediction in pancreatic and colon cancer

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

SEG.A. - Segmentation of the Aorta

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

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.

Pulmonary Nodule Malignancy Prediction

Deep Learning for Malignancy Risk Estimation of Low-Dose Screening CT Detected Pulmonary Nodules

Lobe-Wise Lung Function Estimation from CT

Produces patient-level and lobe-level estimates of DLCO and of FEV1 and FVC pre- and post-bronchodilator