Computer-Aided Diagnosis of Breast Masses using Shape and Margin Radiomic Descriptors and Neural Networks in Dedicated Breast CT Imaging
Domenico Ruben Pangallo
Computer-Aided Diagnosis of Breast Masses using Shape and Margin Radiomic Descriptors and Neural Networks in Dedicated Breast CT Imaging.
Rel. Filippo Molinari, Luca Mainardi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2019
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Abstract
Objective: The goal of this Master Thesis is to design a Computer-Aided Diagnosis (CADx) system for breast mass-like lesion classification in Dedicated Breast CT (DBCT) images, using a quantitative radiomics approach based on newly developed shape and margin imaging biomarkers and a multi-layer perceptron neural network (NN). The clinical motivation behind it is to reduce the number of negative and unnecessary breast biopsies, which constitutes more than 70% of the overall biopsies performed. This project was carried out at the Advanced X-ray Tomographic Imaging (AXTI) Laboratory, Department of Radiology and Nuclear Medicine, Radboud University Medical Center (Nijmegen, The Netherlands). Methods: A traditional radiomic pipeline was implemented.
Therefore, starting from DBCT images and their manual segmentation, the main phases performed were the image cropping to obtain the patches containing the breast lesions, the data augmentation process to increase the number of available patches, the feature extraction based on shape and margin descriptors, and the implementation of the diagnostic Machine Learning (ML) model for the classification of benign and malignant breast masses
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