Spatio-Temporal Modeling for Enhanced Automated De.. (MAMMA)
Spatio-Temporal Modeling for Enhanced Automated Detection and Classification of Non-Mass Lesions in Breast MRI
Date du début: 1 avr. 2014,
Date de fin: 31 mars 2016
The emphasis of this project lies in the development and evaluation of an intelligent and robust computer-assisted system for detecting and diagnosing breast lesions that present a non-mass-like enhancement and thus lead to a substantial improvement of the quality of breast MRI postprocessing, reduce the number of missed or misinterpreted cases leading to false-negative diagnosis, and avoid unnecessary biopsies for benign lesions or observation for malignant lesions.Non-mass-enhancing lesions represent a diagnostic challenge in breast MRI because of the high variance in morphological and kinetic characteristics and have a lower reported specificity and sensitivity than mass-enhancing lesions. Existing image analysis techniques have proven to be insufficient to capture the unique spatio-temporal behavior of these lesions and aid in the automated differential diagnosis of these lesions. We propose to develop, test and evaluate novel techniques for the detection and diagnosis of non-mass-like enhancing lesions and validate them in three specific experiments that will lead to a substantial improvement in diagnostic accuracy and efficiency.The mobility proposed in this project is for Prof. Anke Meyer-Baese, an expert in the field of pattern recognition techniques in medical imaging, to expand her skill set and research portfolio while working on a novel computer-aided diagnosis system for challenging breast lesions at the University of Maastricht, Department of Radiology in Netherlands. Prof. Meyer-Baese is a Full Professor at Florida State University, USA. A number of specific knowledge transferobjectives are outlined in this proposal. This project will have a strong impact on the European Research Area (ERA) through its innovative research goals, focused knowledge transfer and a new international collaboration, the training of medical and engineering students, and outreach measures.
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