BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI
Abstract Accurate segmentation of the prostate peripheral zone (PZ) in T2-weighted MRI is critical for the early detection of prostate cancer. Existing segmentation methods are hindered by significant inter-observer variability (37.4 ± 5.6%), poor boundary localization, and the presence of motion ar...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-025-00467-4 |
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| author | Muhammad Arshad Chengliang Wang Muhammad Wajeeh Us Sima Jamshed Ali Shaikh Hanen Karamti Raed Alharthi Julius Selecky |
| author_facet | Muhammad Arshad Chengliang Wang Muhammad Wajeeh Us Sima Jamshed Ali Shaikh Hanen Karamti Raed Alharthi Julius Selecky |
| author_sort | Muhammad Arshad |
| collection | DOAJ |
| description | Abstract Accurate segmentation of the prostate peripheral zone (PZ) in T2-weighted MRI is critical for the early detection of prostate cancer. Existing segmentation methods are hindered by significant inter-observer variability (37.4 ± 5.6%), poor boundary localization, and the presence of motion artifacts, along with challenges in clinical integration. In this study, we propose BioAug-Net, a novel framework that integrates real-time physiological signal feedback with MRI data, leveraging transformer-based attention mechanisms and a probabilistic clinical decision support system (PCDSS). BioAug-Net features a dual-branch asymmetric attention mechanism: one branch processes spatial MRI features, while the other incorporates temporal sensor signals through a BiGRU-driven adaptive masking module. Additionally, a Markov Decision Process-based PCDSS maps segmentation outputs to clinical PI-RADS scores, with uncertainty quantification. We validated BioAug-Net on a multi-institutional dataset (n=1,542) and demonstrated state-of-the-art performance, achieving a Dice Similarity Coefficient of 89.7% (p < 0.001), sensitivity of 91.2% (p < 0.001), specificity of 88.4% (p < 0.001), and HD95 of 2.14 mm (p < 0.001), outperforming U-Net, Attention U-Net, and TransUNet. Sensor integration improved segmentation accuracy by 12.6% (p < 0.001) and reduced inter-observer variation by 48.3% (p < 0.001). Radiologist evaluations (n=3) confirmed a 15.0% reduction in diagnosis time (p = 0.003) and an increase in inter-reader agreement from K = 0.68 to K = 0.82 (p = 0.001). Our results show that BioAug-Net offers a clinically viable solution for early prostate cancer detection through enhanced physiological coupling and explainable AI diagnostics. |
| format | Article |
| id | doaj-art-371cf78268624ac2985aa8e2b167ec81 |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-371cf78268624ac2985aa8e2b167ec812025-08-20T04:01:53ZengBMCBioData Mining1756-03812025-07-0118114910.1186/s13040-025-00467-4BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRIMuhammad Arshad0Chengliang Wang1Muhammad Wajeeh Us Sima2Jamshed Ali Shaikh3Hanen Karamti4Raed Alharthi5Julius Selecky6Department of Computer Science and Technology, College of Computer Science, Chongqing UniversityDepartment of Computer Science and Technology, College of Computer Science, Chongqing UniversityDepartment of Computer Science and Technology, College of Computer Science, Chongqing UniversityDepartment of Computer Science and Technology, College of Computer Science, Chongqing UniversityDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Engineering, University of Hafr Al-BatinDepartment of Information Management and Business Systems, Comenius University BratislavaAbstract Accurate segmentation of the prostate peripheral zone (PZ) in T2-weighted MRI is critical for the early detection of prostate cancer. Existing segmentation methods are hindered by significant inter-observer variability (37.4 ± 5.6%), poor boundary localization, and the presence of motion artifacts, along with challenges in clinical integration. In this study, we propose BioAug-Net, a novel framework that integrates real-time physiological signal feedback with MRI data, leveraging transformer-based attention mechanisms and a probabilistic clinical decision support system (PCDSS). BioAug-Net features a dual-branch asymmetric attention mechanism: one branch processes spatial MRI features, while the other incorporates temporal sensor signals through a BiGRU-driven adaptive masking module. Additionally, a Markov Decision Process-based PCDSS maps segmentation outputs to clinical PI-RADS scores, with uncertainty quantification. We validated BioAug-Net on a multi-institutional dataset (n=1,542) and demonstrated state-of-the-art performance, achieving a Dice Similarity Coefficient of 89.7% (p < 0.001), sensitivity of 91.2% (p < 0.001), specificity of 88.4% (p < 0.001), and HD95 of 2.14 mm (p < 0.001), outperforming U-Net, Attention U-Net, and TransUNet. Sensor integration improved segmentation accuracy by 12.6% (p < 0.001) and reduced inter-observer variation by 48.3% (p < 0.001). Radiologist evaluations (n=3) confirmed a 15.0% reduction in diagnosis time (p = 0.003) and an increase in inter-reader agreement from K = 0.68 to K = 0.82 (p = 0.001). Our results show that BioAug-Net offers a clinically viable solution for early prostate cancer detection through enhanced physiological coupling and explainable AI diagnostics.https://doi.org/10.1186/s13040-025-00467-4Prostate cancerBioimage processingT2-weighted MRIImage segmentationAttention mechanismClinical decision support system |
| spellingShingle | Muhammad Arshad Chengliang Wang Muhammad Wajeeh Us Sima Jamshed Ali Shaikh Hanen Karamti Raed Alharthi Julius Selecky BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI BioData Mining Prostate cancer Bioimage processing T2-weighted MRI Image segmentation Attention mechanism Clinical decision support system |
| title | BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI |
| title_full | BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI |
| title_fullStr | BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI |
| title_full_unstemmed | BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI |
| title_short | BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI |
| title_sort | bioaug net a bioimage sensor driven attention augmented segmentation framework with physiological coupling for early prostate cancer detection in t2 weighted mri |
| topic | Prostate cancer Bioimage processing T2-weighted MRI Image segmentation Attention mechanism Clinical decision support system |
| url | https://doi.org/10.1186/s13040-025-00467-4 |
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