Prostate magnetic resonance imaging analysis using deep learning

Prostate cancer (PCa) is one of the most common non-skin cancers in men, presenting a global healthcare challenge. Machine learning based prostate magnetic resonance imaging (MRI) analysis includes segmentation, PCa detection, grading, and recoherence prediction. The project focuses on prostate glan...

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Main Author: Li, Huanye
Other Authors: Huang Weimin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/158297
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1582972023-07-07T18:55:01Z Prostate magnetic resonance imaging analysis using deep learning Li, Huanye Huang Weimin Lin Zhiping School of Electrical and Electronic Engineering I2R AStar EZPLin@ntu.edu.sg, MWMHuang@ntu.edu.sg Engineering::Electrical and electronic engineering Prostate cancer (PCa) is one of the most common non-skin cancers in men, presenting a global healthcare challenge. Machine learning based prostate magnetic resonance imaging (MRI) analysis includes segmentation, PCa detection, grading, and recoherence prediction. The project focuses on prostate gland segmentation, prostate lesion segmentation, and prostate lesion classification. Image segmentation is a process of determining regions or boundaries of the area of interest, which is the first step in many clinical decision systems. Prostate lesion detection performs disease detection on given MRIs to assess the probability of certain disease. In this project, we used Transformer UNet as backbone to develop two models, ResUNet and TransUNet for the three tasks. Our experiment shows that the combination of Transformer and UNet yields improved segmentation performance as compared to using pure UNet, while ResNet based CNN outperforms traditional CNN for both segmentation and classification tasks. With the employment of loss function combination, neighboring slices input and hard case augmentation, the proposed algorithm achieved comparable performance as state-of-arts. Chapter 2 "literature review" of this report about machine learning in prostate MRI has been expanded and published in the journal of Diagnostics, by Multidisciplinary Digital Publishing Institute (MDPI), with the paper title “Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities”, where I am the first author. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-31T07:43:26Z 2022-05-31T07:43:26Z 2022 Final Year Project (FYP) Li, H. (2022). Prostate magnetic resonance imaging analysis using deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158297 https://hdl.handle.net/10356/158297 en B3132-211 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Li, Huanye
Prostate magnetic resonance imaging analysis using deep learning
description Prostate cancer (PCa) is one of the most common non-skin cancers in men, presenting a global healthcare challenge. Machine learning based prostate magnetic resonance imaging (MRI) analysis includes segmentation, PCa detection, grading, and recoherence prediction. The project focuses on prostate gland segmentation, prostate lesion segmentation, and prostate lesion classification. Image segmentation is a process of determining regions or boundaries of the area of interest, which is the first step in many clinical decision systems. Prostate lesion detection performs disease detection on given MRIs to assess the probability of certain disease. In this project, we used Transformer UNet as backbone to develop two models, ResUNet and TransUNet for the three tasks. Our experiment shows that the combination of Transformer and UNet yields improved segmentation performance as compared to using pure UNet, while ResNet based CNN outperforms traditional CNN for both segmentation and classification tasks. With the employment of loss function combination, neighboring slices input and hard case augmentation, the proposed algorithm achieved comparable performance as state-of-arts. Chapter 2 "literature review" of this report about machine learning in prostate MRI has been expanded and published in the journal of Diagnostics, by Multidisciplinary Digital Publishing Institute (MDPI), with the paper title “Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities”, where I am the first author.
author2 Huang Weimin
author_facet Huang Weimin
Li, Huanye
format Final Year Project
author Li, Huanye
author_sort Li, Huanye
title Prostate magnetic resonance imaging analysis using deep learning
title_short Prostate magnetic resonance imaging analysis using deep learning
title_full Prostate magnetic resonance imaging analysis using deep learning
title_fullStr Prostate magnetic resonance imaging analysis using deep learning
title_full_unstemmed Prostate magnetic resonance imaging analysis using deep learning
title_sort prostate magnetic resonance imaging analysis using deep learning
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158297
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