Mapping clinically significant lesions from mpMRI using convolution neural network: feasibility assessment in MRI-guided biopsy cases

Joshua Pearlson; Franklin King

doi:10.1117/12.2582228

15 February 2021 Mapping clinically significant lesions from mpMRI using convolution neural network: feasibility assessment in MRI-guided biopsy cases

Joshua Pearlson, Franklin King

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 1159825 (2021) https://doi.org/10.1117/12.2582228
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Purpose: The objective of this paper is to present heatmaps from the likelihood of clinically significant prostate cancer with a deep learning model. This will give radiologists more information on the location of prostate lesions. Methods: 3D Slicer module was developed using a machine learning model to predict pixel-by-pixel PI-RADS scores. The working hypothesis is that the machine learning algorithm will be capable of producing heatmaps with hotspots within a typical size of a lesion with PI-RAD score of 4. Discussion and conclusion: The study provided insight into the future of MRI assessment using Deep Learning models.

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Joshua Pearlson and Franklin King "Mapping clinically significant lesions from mpMRI using convolution neural network: feasibility assessment in MRI-guided biopsy cases", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159825 (15 February 2021); https://doi.org/10.1117/12.2582228

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