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...

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Main Author: Chung, Sheng Hung
Format: Thesis
Language:English
Published: 2022
Subjects:
Online Access:http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf
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author Chung, Sheng Hung
author_facet Chung, Sheng Hung
author_sort Chung, Sheng Hung
collection USM
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.
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spelling usm.eprints-592532023-08-24T06:34:24Z http://eprints.usm.my/59253/ Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation 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 NonPeerReviewed application/pdf en http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf Chung, Sheng Hung (2022) Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Chung, Sheng Hung
Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title 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_short Slow Fusion Triplanar Convolutional Neural Networks For Liver Tumor Segmentation
title_sort slow fusion triplanar convolutional neural networks for liver tumor segmentation
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/59253/1/CHUNG%20SHENG%20HUNG%20-%20TESIS24.pdf
work_keys_str_mv AT chungshenghung slowfusiontriplanarconvolutionalneuralnetworksforlivertumorsegmentation