FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD
Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manu...
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01805-y |
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| author | Liting Fan Yi Lei Feng Song Xiangfei Sun Zhuowei Zhang |
| author_facet | Liting Fan Yi Lei Feng Song Xiangfei Sun Zhuowei Zhang |
| author_sort | Liting Fan |
| collection | DOAJ |
| description | Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manual measurement techniques require considerable human effort and resources from radiologists, and there is a lack of standardized methods for classifying the severity of NAFLD in existing research. Methods To address these challenges, we propose a novel method for NAFLD segmentation and automated severity scoring. The method consists of three key modules: (1) The Semi-automatization nnU-Net Module (SNM) constructs a high-quality dataset by combining manual annotations with semi-automated refinement; (2) The Focal Feature Fusion Swin-Unet Module (FSM) enhances liver and spleen segmentation through multi-scale feature fusion and Swin Transformer-based architectures; (3) The Automated Severity Scoring Module (ASSM) integrates segmentation results with radiological features to classify NAFLD severity. These modules are embedded in a Flask-RESTful API-based system, enabling users to upload abdominal CT data for automated preprocessing, segmentation, and scoring. Results The Focal Feature Fusion Swin-Unet (FF Swin-Unet) method significantly improves segmentation accuracy, achieving a Dice similarity coefficient (DSC) of 95.64% and a 95th percentile Hausdorff distance (HD95) of 15.94. The accuracy of the automated severity scoring is 90%. With model compression and ONNX deployment, the evaluation speed for each case is approximately 5 seconds. Compared to manual diagnosis, the system can process a large volume of data simultaneously, rapidly, and efficiently while maintaining the same level of diagnostic accuracy, significantly reducing the workload of medical professionals. Conclusions Our research demonstrates that the proposed system has high accuracy in processing large volumes of CT data and providing automated NAFLD severity scores quickly and efficiently. This method has the potential to significantly reduce the workload of medical professionals and holds immense clinical application potential. |
| format | Article |
| id | doaj-art-720caaf9dd76436494f6f4a30dcc47ec |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-720caaf9dd76436494f6f4a30dcc47ec2025-08-20T04:02:44ZengBMCBMC Medical Imaging1471-23422025-07-0125111610.1186/s12880-025-01805-yFF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLDLiting Fan0Yi Lei1Feng Song2Xiangfei Sun3Zhuowei Zhang4School of Basic Medicine, Shanxi Medical UniversitySchool of Software Engineering, Faculty of Information Technology, Beijing University of TechnologySchool of Basic Medicine, Shanxi Medical UniversityShanxi Key Laboratory of Big Data for Clinical Decision Research, School of Management, Shanxi Medical UniversityCollege of Medical Imaging, Shanxi Medical UniversityAbstract Background Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and assessing its severity. However, current manual measurement techniques require considerable human effort and resources from radiologists, and there is a lack of standardized methods for classifying the severity of NAFLD in existing research. Methods To address these challenges, we propose a novel method for NAFLD segmentation and automated severity scoring. The method consists of three key modules: (1) The Semi-automatization nnU-Net Module (SNM) constructs a high-quality dataset by combining manual annotations with semi-automated refinement; (2) The Focal Feature Fusion Swin-Unet Module (FSM) enhances liver and spleen segmentation through multi-scale feature fusion and Swin Transformer-based architectures; (3) The Automated Severity Scoring Module (ASSM) integrates segmentation results with radiological features to classify NAFLD severity. These modules are embedded in a Flask-RESTful API-based system, enabling users to upload abdominal CT data for automated preprocessing, segmentation, and scoring. Results The Focal Feature Fusion Swin-Unet (FF Swin-Unet) method significantly improves segmentation accuracy, achieving a Dice similarity coefficient (DSC) of 95.64% and a 95th percentile Hausdorff distance (HD95) of 15.94. The accuracy of the automated severity scoring is 90%. With model compression and ONNX deployment, the evaluation speed for each case is approximately 5 seconds. Compared to manual diagnosis, the system can process a large volume of data simultaneously, rapidly, and efficiently while maintaining the same level of diagnostic accuracy, significantly reducing the workload of medical professionals. Conclusions Our research demonstrates that the proposed system has high accuracy in processing large volumes of CT data and providing automated NAFLD severity scores quickly and efficiently. This method has the potential to significantly reduce the workload of medical professionals and holds immense clinical application potential.https://doi.org/10.1186/s12880-025-01805-yNon-alcoholic fatty liver disease(NAFLD)CT imagesDeep learningInstance segmetationSeverity scoringAided diagnosis |
| spellingShingle | Liting Fan Yi Lei Feng Song Xiangfei Sun Zhuowei Zhang FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD BMC Medical Imaging Non-alcoholic fatty liver disease(NAFLD) CT images Deep learning Instance segmetation Severity scoring Aided diagnosis |
| title | FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD |
| title_full | FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD |
| title_fullStr | FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD |
| title_full_unstemmed | FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD |
| title_short | FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD |
| title_sort | ff swin unet a strategy for automated segmentation and severity scoring of nafld |
| topic | Non-alcoholic fatty liver disease(NAFLD) CT images Deep learning Instance segmetation Severity scoring Aided diagnosis |
| url | https://doi.org/10.1186/s12880-025-01805-y |
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