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Convolutional neural networks for Magnetic Resonance Image prostate segmentation

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thesis
posted on 28.03.2022, 14:06 by Tahareh Hassan Zadeh Koohi
Digital medical image segmentation is the process of partitioning an image into several discrete and homogeneous regions. Segmentation is needed to find the boundary of the prostate either automatically or semi-automatically. One of the most accurate and non-invasive prostate imaging methods is Magnetic Resonance Imaging (MRI) which is usually employed for the prostate image segmentation and/or possible prostate anomalies detection. In this research, to improve the Fully Convolutional Neural Network (FCNN) performance for prostate MRI segmentation, we analyse various structures of shortcut connections as well as the size of a deep network. We suggest six different deep 2D network structures for automatic MRI prostate segmentation based on FCNN. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyse the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation, without any further post-processing module nor pretraining on publicly available data.

History

Table of Contents

1 Introduction -- 2 Background and literature review -- 3 Proposed models -- 4 Data analysis and parameters setting -- 5 Experimental results -- 6 Conclusion and future work.

Notes

Theoretical thesis. Bibliography: pages 77-89

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award

2018

Principal Supervisor

Len Hamey

Rights

Copyright Tahareh Hassan Zadeh Koohi Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xx, 89 pages) illustrations

Former Identifiers

mq:72110 http://hdl.handle.net/1959.14/1281485