A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation
Liver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual d...
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| Format: | Article |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/8365349 |
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| author | Justice Kwame Appati Nathanael Ayirebaje Azuponga Leonard Mensah Boante Joseph Agyeapong Mensah |
| author_facet | Justice Kwame Appati Nathanael Ayirebaje Azuponga Leonard Mensah Boante Joseph Agyeapong Mensah |
| author_sort | Justice Kwame Appati |
| collection | DOAJ |
| description | Liver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual differentiation. Manual identification of tumors in the liver is time-consuming, intricate, and susceptible to errors with potential repercussions for patient care. While machine learning–based approaches have emerged for liver organ recognition and segmenting the tumor, they continue to face challenges related to recognition accuracy and the inability to distinguish tumors of varied sizes. To solve the problems, a multiattention network made up of cascaded ResUNet and U-Net with attention mechanisms was proposed in this study. We investigated liver tumor segmentation with various configurations of U-Net, ResUNet, U-Net with attention mechanisms, and ResUNet with attention mechanisms on augmented and nonaugmented data. We used the 3Dircadb dataset for training and validation purposes, and the proposed method was evaluated on dice score, intersection of union (IoU), recall, and precision. The performance metrics achieved with this method on the dataset are as follows: approximately 0.89 for the dice coefficient, 0.90 for IoU, 0.93 for recall, and 0.96 for precision in the case of liver segmentation without data augmentation and 0.92, 0.90, 0.92, and 0.94, respectively, for dice score, IoU, recall, and precision with data augmentation. For tumor segmentation, the metrics include 0.70 for dice coefficient, 0.61 for IoU, 0.91 for recall, and 0.94 for precision when the data were augmented but 0.83 for dice score, 0.78 for IoU, and 0.89 and 0.90, respectively, for recall and precision. |
| format | Article |
| id | doaj-art-c295f8da7ad646959cd5da4bf676dfaf |
| institution | OA Journals |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-c295f8da7ad646959cd5da4bf676dfaf2025-08-20T02:18:59ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/8365349A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor SegmentationJustice Kwame Appati0Nathanael Ayirebaje Azuponga1Leonard Mensah Boante2Joseph Agyeapong Mensah3Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer Science and Information SystemLiver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual differentiation. Manual identification of tumors in the liver is time-consuming, intricate, and susceptible to errors with potential repercussions for patient care. While machine learning–based approaches have emerged for liver organ recognition and segmenting the tumor, they continue to face challenges related to recognition accuracy and the inability to distinguish tumors of varied sizes. To solve the problems, a multiattention network made up of cascaded ResUNet and U-Net with attention mechanisms was proposed in this study. We investigated liver tumor segmentation with various configurations of U-Net, ResUNet, U-Net with attention mechanisms, and ResUNet with attention mechanisms on augmented and nonaugmented data. We used the 3Dircadb dataset for training and validation purposes, and the proposed method was evaluated on dice score, intersection of union (IoU), recall, and precision. The performance metrics achieved with this method on the dataset are as follows: approximately 0.89 for the dice coefficient, 0.90 for IoU, 0.93 for recall, and 0.96 for precision in the case of liver segmentation without data augmentation and 0.92, 0.90, 0.92, and 0.94, respectively, for dice score, IoU, recall, and precision with data augmentation. For tumor segmentation, the metrics include 0.70 for dice coefficient, 0.61 for IoU, 0.91 for recall, and 0.94 for precision when the data were augmented but 0.83 for dice score, 0.78 for IoU, and 0.89 and 0.90, respectively, for recall and precision.http://dx.doi.org/10.1155/2024/8365349 |
| spellingShingle | Justice Kwame Appati Nathanael Ayirebaje Azuponga Leonard Mensah Boante Joseph Agyeapong Mensah A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation Applied Computational Intelligence and Soft Computing |
| title | A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation |
| title_full | A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation |
| title_fullStr | A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation |
| title_full_unstemmed | A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation |
| title_short | A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation |
| title_sort | multiattention resunet and modified u net architecture for liver tumor segmentation |
| url | http://dx.doi.org/10.1155/2024/8365349 |
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