Demonstrator of SATORI Lung Analysis with integrated image quality analysis

Demonstration of lung analysis in SATORI with image quality analysis and integration of CORADS and severy score

LUMIC

The LUMIC challenge tests the accuracy in registration between pre- and post-contrast CT chest images for algorithms, using an anthropomophic digital phantom.

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

Learn2Reg

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

MELA2022

MICCAI 2022 MELA Challenge: A Large-Scale Detection Benchmark of 1,100 CT Scans for Mediastinal Lesion Analysis

autoPET

Automatic lesion segmentation in whole-body FDG-PET/CT

ACROBAT 2023

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

autoPET-II

Automated Lesion Segmentation in PET/CT - Domain Generalization

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.

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.

Universal Lesion Segmentation Challenge '23

AutoPET III

Pulmonary Lobe Segmentation

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

Pulmonary Nodule Malignancy Prediction

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

Deep-Learning-Based CT Lung Registration

Registration of inspiration and expiration lung CT scans using a neural network trained with multiple anatomical constraints.

corrField

Correspondence fields for large motion image registration

Airway Anatomical Labeling

Given an airway segmentation where individual airway branches are extracted, this algorithm will automatically find 18 segmental branches, including 8 from the left lung (LB1+2, LB3, LB4, LB5, LB6, LB7+8, LB9, and LB10) and 10 from the right lung (RB1-10).

BDAV_Y (Y. Yuan, et al.; Australia) algorithm trained on PI-CAI: Private and Public Training Dataset

Universal Lesion Segmentation [ULS23 Baseline]

Univeral Lesion Segmentation algorithm for Computed Tomography