ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation
Background: Automatic segmentation of liver regions as well as liver lesions such as hepatocellular carcinoma (HCC) from computed tomography (CT) images is critical for accurate diagnosis and therapy planning. With the advent of deep learning techniques such as transformers, computer-aided diagnosti...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072178/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849430123464359936 |
|---|---|
| author | Chukwuemeka Clinton Atabansi Hui Li Sheng Wang Jing Nie Haijun Liu Bo Xu Xichuan Zhou Dewei Li |
| author_facet | Chukwuemeka Clinton Atabansi Hui Li Sheng Wang Jing Nie Haijun Liu Bo Xu Xichuan Zhou Dewei Li |
| author_sort | Chukwuemeka Clinton Atabansi |
| collection | DOAJ |
| description | Background: Automatic segmentation of liver regions as well as liver lesions such as hepatocellular carcinoma (HCC) from computed tomography (CT) images is critical for accurate diagnosis and therapy planning. With the advent of deep learning techniques such as transformers, computer-aided diagnostic tools (CADs) have the potential to increase the accuracy of liver tumor diagnosis, progression, and treatment planning. However, two major challenges remain: 1) existing models struggle to extract robust spatial features for accurate liver and liver lesion segmentation, and 2) publicly available liver datasets with HCC annotations are limited. Methods: We first present a new liver dataset acquired from Chongqing University Cancer Hospital (CCH-LHCC-CT) with HCC annotations. Second, we developed a novel deep learning architecture (ICT-Net), which is constructed based on a pretrained transformer encoder in conjunction with an advanced feature upscaling and enhanced convolution-transformer decoder formation. Results: We performed liver and liver tumor segmentation on the CCH-LHCC-CT and three public CT liver datasets. The proposed ICT-Net architecture achieves superior accuracy (higher ACC/DSC/IoU, lower HD95) across all datasets. Conclusions: We construct a novel deep-learning architecture that produces robust information for liver and liver tumor segmentation. The statistical and visual results demonstrate that the proposed ICT-Net outperforms other existing approaches investigated in this study in terms of ACC, DSC, and IoU. Clinical Translation Statement: ICT-Net enhances surgical planning accuracy through precise tumor margin delineation and improves therapy response assessment reliability, which holds meaningful promise to support more precise and effective clinical therapeutic strategies for patients with HCC. |
| format | Article |
| id | doaj-art-e1d4ffdcf9eb4506b6702506e148b394 |
| institution | Kabale University |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-e1d4ffdcf9eb4506b6702506e148b3942025-08-20T03:28:06ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-011331032210.1109/JTEHM.2025.358647011072178ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region SegmentationChukwuemeka Clinton Atabansi0https://orcid.org/0000-0003-1136-2210Hui Li1https://orcid.org/0000-0001-7287-2690Sheng Wang2Jing Nie3https://orcid.org/0000-0001-9872-9286Haijun Liu4https://orcid.org/0000-0001-5782-4543Bo Xu5https://orcid.org/0000-0001-8693-3060Xichuan Zhou6https://orcid.org/0000-0002-3304-3045Dewei Li7School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaDepartment of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaChongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaDepartment of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, ChinaBackground: Automatic segmentation of liver regions as well as liver lesions such as hepatocellular carcinoma (HCC) from computed tomography (CT) images is critical for accurate diagnosis and therapy planning. With the advent of deep learning techniques such as transformers, computer-aided diagnostic tools (CADs) have the potential to increase the accuracy of liver tumor diagnosis, progression, and treatment planning. However, two major challenges remain: 1) existing models struggle to extract robust spatial features for accurate liver and liver lesion segmentation, and 2) publicly available liver datasets with HCC annotations are limited. Methods: We first present a new liver dataset acquired from Chongqing University Cancer Hospital (CCH-LHCC-CT) with HCC annotations. Second, we developed a novel deep learning architecture (ICT-Net), which is constructed based on a pretrained transformer encoder in conjunction with an advanced feature upscaling and enhanced convolution-transformer decoder formation. Results: We performed liver and liver tumor segmentation on the CCH-LHCC-CT and three public CT liver datasets. The proposed ICT-Net architecture achieves superior accuracy (higher ACC/DSC/IoU, lower HD95) across all datasets. Conclusions: We construct a novel deep-learning architecture that produces robust information for liver and liver tumor segmentation. The statistical and visual results demonstrate that the proposed ICT-Net outperforms other existing approaches investigated in this study in terms of ACC, DSC, and IoU. Clinical Translation Statement: ICT-Net enhances surgical planning accuracy through precise tumor margin delineation and improves therapy response assessment reliability, which holds meaningful promise to support more precise and effective clinical therapeutic strategies for patients with HCC.https://ieeexplore.ieee.org/document/11072178/Vision transformerliver segmentationhepatocellular carcinoma segmentationliver cancer diagnosisCT images |
| spellingShingle | Chukwuemeka Clinton Atabansi Hui Li Sheng Wang Jing Nie Haijun Liu Bo Xu Xichuan Zhou Dewei Li ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation IEEE Journal of Translational Engineering in Health and Medicine Vision transformer liver segmentation hepatocellular carcinoma segmentation liver cancer diagnosis CT images |
| title | ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation |
| title_full | ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation |
| title_fullStr | ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation |
| title_full_unstemmed | ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation |
| title_short | ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation |
| title_sort | ict net an integrated convolution and transformer based network for complex liver and liver tumor region segmentation |
| topic | Vision transformer liver segmentation hepatocellular carcinoma segmentation liver cancer diagnosis CT images |
| url | https://ieeexplore.ieee.org/document/11072178/ |
| work_keys_str_mv | AT chukwuemekaclintonatabansi ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT huili ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT shengwang ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT jingnie ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT haijunliu ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT boxu ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT xichuanzhou ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation AT deweili ictnetanintegratedconvolutionandtransformerbasednetworkforcomplexliverandlivertumorregionsegmentation |