Medical Image Segmentation

AISTAT combines a pre-trained U-Net deep learning model with a statistical contour-based framework, enabling semi-automated segmentation that is both interpretable and clinically actionable. Unlike standard pixel-based methods, it provides contour-level outputs suitable for radiotherapy (RT) including intensity modulated (IMRT) and includes a traffic light-style uncertainty measure, supporting more informed clinical decisions.The tool has already been trained on publicly available AUTOPET dataset and currently being tested on 1,400 non-small cell lung cancer and 60 lymphoma patients. In this GKEF project we will focus exclusively on H&amp;N cancers, using both the publicly available HECKTOR dataset and a real-world set of 200 patients from NHSGGC, including radiotherapy plans to provide a demonstration of clinical utility on real-world tumours that will in turn provide a platform for further investment and development.Goal Combine Statistical Learing with AI to provide a new framework for probabilistic contouring and Uncertainty QuantificationResearchers Wenhui Zhang Working on probabilistic contouring of medical images More Info </cent...