Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling
Prostate cancer, a prevalent malignancy affecting males globally, underscores the critical need for precise prostate segmentation in diagnostic imaging. However, accurate delineation via MRI still faces several challenges: (1) The distinction of the prostate from surrounding soft tissues is impeded...
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MDPI AG
2025-02-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/2/61 |
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| author | Jingyi Zhu Xukun Zhang Xiao Luo Zhiji Zheng Kun Zhou Yanlan Kang Haiqing Li Daoying Geng |
| author_facet | Jingyi Zhu Xukun Zhang Xiao Luo Zhiji Zheng Kun Zhou Yanlan Kang Haiqing Li Daoying Geng |
| author_sort | Jingyi Zhu |
| collection | DOAJ |
| description | Prostate cancer, a prevalent malignancy affecting males globally, underscores the critical need for precise prostate segmentation in diagnostic imaging. However, accurate delineation via MRI still faces several challenges: (1) The distinction of the prostate from surrounding soft tissues is impeded by subtle boundaries in MRI images. (2) Regions such as the apex and base of the prostate exhibit inherent blurriness, which complicates edge extraction and precise segmentation. The objective of this study was to precisely delineate the borders of the prostate including the apex and base regions. This study introduces a multi-scale context modeling module to enhance boundary pixel representation, thus reducing the impact of irrelevant features on segmentation outcomes. Utilizing a first-in-first-out dynamic adjustment mechanism, the proposed methodology optimizes feature vector selection, thereby enhancing segmentation outcomes for challenging apex and base regions of the prostate. Segmentation of the prostate on 2175 clinically annotated MRI datasets demonstrated that our proposed MCM-UNet outperforms existing methods. The Average Symmetric Surface Distance (ASSD) and Dice similarity coefficient (DSC) for prostate segmentation were 0.58 voxels and 91.71%, respectively. The prostate segmentation results closely matched those manually delineated by experienced radiologists. Consequently, our method significantly enhances the accuracy of prostate segmentation and holds substantial significance in the diagnosis and treatment of prostate cancer. |
| format | Article |
| id | doaj-art-6b1e0bdca3fd4840b1470a606dff736a |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-6b1e0bdca3fd4840b1470a606dff736a2025-08-20T02:44:39ZengMDPI AGJournal of Imaging2313-433X2025-02-011126110.3390/jimaging11020061Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context ModelingJingyi Zhu0Xukun Zhang1Xiao Luo2Zhiji Zheng3Kun Zhou4Yanlan Kang5Haiqing Li6Daoying Geng7Academy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai 200400, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaProstate cancer, a prevalent malignancy affecting males globally, underscores the critical need for precise prostate segmentation in diagnostic imaging. However, accurate delineation via MRI still faces several challenges: (1) The distinction of the prostate from surrounding soft tissues is impeded by subtle boundaries in MRI images. (2) Regions such as the apex and base of the prostate exhibit inherent blurriness, which complicates edge extraction and precise segmentation. The objective of this study was to precisely delineate the borders of the prostate including the apex and base regions. This study introduces a multi-scale context modeling module to enhance boundary pixel representation, thus reducing the impact of irrelevant features on segmentation outcomes. Utilizing a first-in-first-out dynamic adjustment mechanism, the proposed methodology optimizes feature vector selection, thereby enhancing segmentation outcomes for challenging apex and base regions of the prostate. Segmentation of the prostate on 2175 clinically annotated MRI datasets demonstrated that our proposed MCM-UNet outperforms existing methods. The Average Symmetric Surface Distance (ASSD) and Dice similarity coefficient (DSC) for prostate segmentation were 0.58 voxels and 91.71%, respectively. The prostate segmentation results closely matched those manually delineated by experienced radiologists. Consequently, our method significantly enhances the accuracy of prostate segmentation and holds substantial significance in the diagnosis and treatment of prostate cancer.https://www.mdpi.com/2313-433X/11/2/61prostate segmentationcontext modeling moduledynamic adjustment mechanismT2-weighted imaging |
| spellingShingle | Jingyi Zhu Xukun Zhang Xiao Luo Zhiji Zheng Kun Zhou Yanlan Kang Haiqing Li Daoying Geng Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling Journal of Imaging prostate segmentation context modeling module dynamic adjustment mechanism T2-weighted imaging |
| title | Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling |
| title_full | Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling |
| title_fullStr | Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling |
| title_full_unstemmed | Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling |
| title_short | Accurate Prostate Segmentation in Large-Scale Magnetic Resonance Imaging Datasets via First-in-First-Out Feature Memory and Multi-Scale Context Modeling |
| title_sort | accurate prostate segmentation in large scale magnetic resonance imaging datasets via first in first out feature memory and multi scale context modeling |
| topic | prostate segmentation context modeling module dynamic adjustment mechanism T2-weighted imaging |
| url | https://www.mdpi.com/2313-433X/11/2/61 |
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