Cross modality medical image synthesis for improving liver segmentation
Deep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labelled datasets limits the development of deep learning-based CAD systems. Generative Adversarial Networks (GANs), in particular, CycleGAN, can be used to gen...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2476702 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850140005182210048 |
|---|---|
| author | Muhammad Rafiq Hazrat Ali Ghulam Mujtaba Zubair Shah Shoaib Azmat |
| author_facet | Muhammad Rafiq Hazrat Ali Ghulam Mujtaba Zubair Shah Shoaib Azmat |
| author_sort | Muhammad Rafiq |
| collection | DOAJ |
| description | Deep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labelled datasets limits the development of deep learning-based CAD systems. Generative Adversarial Networks (GANs), in particular, CycleGAN, can be used to generate new cross-domain images without paired training data. However, most CycleGAN-based synthesis methods lack the potential to overcome alignment and asymmetry between the input and generated data. We propose a two-stage technique for the synthesis of abdominal MRI using cross-modality translation of abdominal CT. We show that the synthetic data can help improve the performance of the liver segmentation network. We increase the number of abdominal MRI images through cross-modality image transformation of unpaired CT images using a CycleGAN inspired deformation invariant network called EssNet. Subsequently, we combine the synthetic MRI images with the original MRI images and use them to improve the accuracy of the U-Net on a liver segmentation task. We train the U-Net on real MRI images and then on real and synthetic MRI images. Consequently, by comparing both scenarios, we achieve an improvement in the performance of U-Net. In summary, the improvement achieved in the Intersection over Union (IoU) is 1.17%. The results show the potential to address the data scarcity challenge in medical imaging. |
| format | Article |
| id | doaj-art-59df717344574e8c8eb7cdc0a5064bf8 |
| institution | OA Journals |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-59df717344574e8c8eb7cdc0a5064bf82025-08-20T02:30:00ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2476702Cross modality medical image synthesis for improving liver segmentationMuhammad Rafiq0Hazrat Ali1Ghulam Mujtaba2Zubair Shah3Shoaib Azmat4Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanComputing Science and Mathematics, University of Stirling, Stirling, UKDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDeep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labelled datasets limits the development of deep learning-based CAD systems. Generative Adversarial Networks (GANs), in particular, CycleGAN, can be used to generate new cross-domain images without paired training data. However, most CycleGAN-based synthesis methods lack the potential to overcome alignment and asymmetry between the input and generated data. We propose a two-stage technique for the synthesis of abdominal MRI using cross-modality translation of abdominal CT. We show that the synthetic data can help improve the performance of the liver segmentation network. We increase the number of abdominal MRI images through cross-modality image transformation of unpaired CT images using a CycleGAN inspired deformation invariant network called EssNet. Subsequently, we combine the synthetic MRI images with the original MRI images and use them to improve the accuracy of the U-Net on a liver segmentation task. We train the U-Net on real MRI images and then on real and synthetic MRI images. Consequently, by comparing both scenarios, we achieve an improvement in the performance of U-Net. In summary, the improvement achieved in the Intersection over Union (IoU) is 1.17%. The results show the potential to address the data scarcity challenge in medical imaging.https://www.tandfonline.com/doi/10.1080/21681163.2025.2476702Computer aided diagnosisCycleGANmedical imagingMRIsegmentation |
| spellingShingle | Muhammad Rafiq Hazrat Ali Ghulam Mujtaba Zubair Shah Shoaib Azmat Cross modality medical image synthesis for improving liver segmentation Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Computer aided diagnosis CycleGAN medical imaging MRI segmentation |
| title | Cross modality medical image synthesis for improving liver segmentation |
| title_full | Cross modality medical image synthesis for improving liver segmentation |
| title_fullStr | Cross modality medical image synthesis for improving liver segmentation |
| title_full_unstemmed | Cross modality medical image synthesis for improving liver segmentation |
| title_short | Cross modality medical image synthesis for improving liver segmentation |
| title_sort | cross modality medical image synthesis for improving liver segmentation |
| topic | Computer aided diagnosis CycleGAN medical imaging MRI segmentation |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2476702 |
| work_keys_str_mv | AT muhammadrafiq crossmodalitymedicalimagesynthesisforimprovingliversegmentation AT hazratali crossmodalitymedicalimagesynthesisforimprovingliversegmentation AT ghulammujtaba crossmodalitymedicalimagesynthesisforimprovingliversegmentation AT zubairshah crossmodalitymedicalimagesynthesisforimprovingliversegmentation AT shoaibazmat crossmodalitymedicalimagesynthesisforimprovingliversegmentation |