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Biomedical Communicator | Translating clinical and biomedical research across surgical and medical disciplines
Location
University Hospital Limerick, Limerick
University of Limerick
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Biomedical Communicator | Translating clinical and biomedical research across surgical and medical disciplines
University Hospital Limerick, Limerick
University of Limerick
This project focuses on the development of a medical AI system for automatic segmentation of DICOM imaging data, specifically targeting the mesenteric domain. Working in conjunction with TotalSegmentator, the model enables detailed anatomical segmentation of abdominal structures and supports advanced quantification of abdominal fat deposits, including mesenteric, retromesenteric, and subcutaneous fat. These quantitative outputs can be used to build patient‑specific anatomical models and to investigate relationships between fat distribution, health, and disease. The project demonstrates the potential of AI‑driven imaging tools to enhance clinical research and support precision medicine.
Exploring the living body can only be done in detail by specialists such as Radiologists and Surgeons. If such exploration were accessible to the non-specialist, it would accelerate knowledge generation and discovery.
The problem with current CT reconstructions is that only the radio opaque parts of the CT are reconstructed and the radio lucent areas are generally ignored. This leaves large gaps in the reconstructions, and these gaps mainly consist of fat depots.
We exploit the domain-based organisation of the abdomen to develop a framework by which to generate and distribute the first accurate and complete digital models of the abdomen of the living, individual subject, from computerised tomographic imaging.
Digital Imaging and Communications in Medicine
MONAI Label is an intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets. By utilizing user interactions, MONAI Label trains an AI model for a specific task and continuously learns and updates that model as it receives additional annotated images.
