A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet
According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magne...
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MDPI AG
2022-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/8/368 |
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| author | Hameedur Rahman Tanvir Fatima Naik Bukht Azhar Imran Junaid Tariq Shanshan Tu Abdulkareeem Alzahrani |
| author_facet | Hameedur Rahman Tanvir Fatima Naik Bukht Azhar Imran Junaid Tariq Shanshan Tu Abdulkareeem Alzahrani |
| author_sort | Hameedur Rahman |
| collection | DOAJ |
| description | According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors. |
| first_indexed | 2024-03-09T10:01:30Z |
| format | Article |
| id | doaj.art-67da81f04cd6449f8231a72ba006a10c |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T10:01:30Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-67da81f04cd6449f8231a72ba006a10c2023-12-01T23:25:06ZengMDPI AGBioengineering2306-53542022-08-019836810.3390/bioengineering9080368A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNetHameedur Rahman0Tanvir Fatima Naik Bukht1Azhar Imran2Junaid Tariq3Shanshan Tu4Abdulkareeem Alzahrani5Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, PakistanDepartment of Computer Science, Air University, PAF Complex, Islamabad 44000, PakistanDepartment of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, PakistanDepartment of Computer Science, National University of Modern Languages (NUML), Rawalpindi Campus, Islamabad 44000, PakistanFaculty of Information Technology, Beijing University of Technology, Beijing 100024, ChinaComputer Engineering and Science Department, Faculty of Computer Science and Information Technology, Al Baha University, Al Baha 65515, Saudi ArabiaAccording to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.https://www.mdpi.com/2306-5354/9/8/368computed tomographydeep learningliver segmentationmedical imagingresidual networktumor segmentation |
| spellingShingle | Hameedur Rahman Tanvir Fatima Naik Bukht Azhar Imran Junaid Tariq Shanshan Tu Abdulkareeem Alzahrani A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet Bioengineering computed tomography deep learning liver segmentation medical imaging residual network tumor segmentation |
| title | A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet |
| title_full | A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet |
| title_fullStr | A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet |
| title_full_unstemmed | A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet |
| title_short | A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet |
| title_sort | deep learning approach for liver and tumor segmentation in ct images using resunet |
| topic | computed tomography deep learning liver segmentation medical imaging residual network tumor segmentation |
| url | https://www.mdpi.com/2306-5354/9/8/368 |
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