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Integration of Deep Convolutional Networks and Active Shape Models for automatic prostate segmentation.
Rel. Filippo Molinari, Massimo Salvi, Bruno De Santi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
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Abstract
Prostate cancer is the most common cancer in the world for what concerns the male population. There are several types of tests to diagnose it, including screening tests based on the recognition of the PSA antigen, urological approaches with ultrasounds or other techniques that make use of different acquisition modalities. Among these, MRI proves to have the greatest accuracy in recognizing the prostate and tissue irregularities within it. The diagnosis of a pathology affecting this organ is carried out using CAD systems that use automatic segmentation algorithms as a support to recognize at first the organ in its entirety or a region in which it is most likely to be found within the image.
The use of automatic algorithms for prostate segmentation allows to bypass the huge workload on technicians as the data available for processing is enormous and it needs rapid and less operator-dependent techniques
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