Demo archive

Demo archive with radiology images

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

Learn2Reg

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

AIROGS

Artificial Intelligence for RObust Glaucoma Screening Challenge

TDSC-ABUS2023

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound

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

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.

corrField

Correspondence fields for large motion image registration