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Automated Detection of Renal Masses in Contrast-Enhanced MRI using Deep Learning Methods

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Title: Automated Detection of Renal Masses in Contrast-Enhanced MRI using Deep Learning Methods
Author: Agarwal, Anush
Department: School of Engineering
Program: Engineering
Advisor: Ukwatta, Eranga
Abstract: Multiparametric MRI based assessment of renal masses in the kidney has the potential to improve tumour classification accuracy and as a result improve treatment outcomes for renal cancer patients. Despite the increased use of MRI for renal mass assessment, the use of deep learning techniques to detect and classify renal masses using this modality remains unexplored. I propose a fully automated computer aided detection algorithm that identifies spatially separated renal masses in abdominal contrast-enhanced nephrographic phase MRI volumes. A cascaded series of U-Net models is used, with the first step isolating kidney boundaries with a Dice score of 91.20%. These boundaries are used to identify instances of renal masses with a mass-wise precision of 83.3% and a recall of 86.2%. The proposed algorithm can serve as a localizer for future work that incorporates multiparametric MRI data to classify tumours based on pathology.
URI: https://hdl.handle.net/10214/26311
Date: 2021-08
Rights: Attribution-NonCommercial 4.0 International


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Attribution-NonCommercial 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International