Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation

According to the World Health Organization (WHO) report, liver tumor is one of the leading cause of death in all cancerous disease reported worldwide, with fatalities rate of 745,000 patients in 2014, 788,000 patients in 2015 and 782,000 patients in 2018 respectively. Liver tumor diagnosis and surge...

Full description

Saved in:

Bibliographic Details
Main Author:	Chung, Sheng Hung
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.59253
record_format	uketd_dc
spelling	my-usm-ep.592532023-08-24T06:34:24Z Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation 2022-04 Chung, Sheng Hung QA75.5-76.95 Electronic computers. Computer science According to the World Health Organization (WHO) report, liver tumor is one of the leading cause of death in all cancerous disease reported worldwide, with fatalities rate of 745,000 patients in 2014, 788,000 patients in 2015 and 782,000 patients in 2018 respectively. Liver tumor diagnosis and surgery planning are commonly performed with Computed Tomography (CT) scan to assist doctors in evaluating liver tumor and planning of the relevant treatment for the patients. However, there are challenges faced in liver tumor segmentation such as (i) similar intensities between liver tumor and liver tissues, (ii) small and indeterminate liver tumor which are difficult to characterize and (iii) liver tumor with irregular shapes and boundaries. Therefore, an accurate liver tumor detection and segmentation is a crucial prerequisite for liver tumor diagnosis, surgery and treatment planning. In this study, we demonstrate the use of multiple views including axial, sagittal and coronal images as the inputs for Convolutional Neural Networks for liver tumor segmentation, named as Triplanar Convolutional Neural Networks. Our designed network model, Triplanar Convolutional Neural Networks utilize different views of liver CT images to extract discriminative features from the Voxel of Interest (VOI) to classify liver tumor from a healthy liver region in the Liver CT dataset obtained from MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. 2022-04 Thesis http://eprints.usm.my/59253/ http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Chung, Sheng Hung Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
description	According to the World Health Organization (WHO) report, liver tumor is one of the leading cause of death in all cancerous disease reported worldwide, with fatalities rate of 745,000 patients in 2014, 788,000 patients in 2015 and 782,000 patients in 2018 respectively. Liver tumor diagnosis and surgery planning are commonly performed with Computed Tomography (CT) scan to assist doctors in evaluating liver tumor and planning of the relevant treatment for the patients. However, there are challenges faced in liver tumor segmentation such as (i) similar intensities between liver tumor and liver tissues, (ii) small and indeterminate liver tumor which are difficult to characterize and (iii) liver tumor with irregular shapes and boundaries. Therefore, an accurate liver tumor detection and segmentation is a crucial prerequisite for liver tumor diagnosis, surgery and treatment planning. In this study, we demonstrate the use of multiple views including axial, sagittal and coronal images as the inputs for Convolutional Neural Networks for liver tumor segmentation, named as Triplanar Convolutional Neural Networks. Our designed network model, Triplanar Convolutional Neural Networks utilize different views of liver CT images to extract discriminative features from the Voxel of Interest (VOI) to classify liver tumor from a healthy liver region in the Liver CT dataset obtained from MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Chung, Sheng Hung
author_facet	Chung, Sheng Hung
author_sort	Chung, Sheng Hung
title	Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_short	Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_full	Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_fullStr	Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_full_unstemmed	Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_sort	slow fusion triplanar convolutional neural networks for liver tumor segmentation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2022
url	http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf
_version_	1776101273061294080

Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation

Similar Items