MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution
Background and objectivesThis paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1563959/full |
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| author | Xian Wang Wenzhi Zeng Junzeng Xu Senhao Zhang Yuexing Gu Benhui Li Xueyang Wang |
| author_facet | Xian Wang Wenzhi Zeng Junzeng Xu Senhao Zhang Yuexing Gu Benhui Li Xueyang Wang |
| author_sort | Xian Wang |
| collection | DOAJ |
| description | Background and objectivesThis paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary classification tasks to enhance feature representation and computational efficiency.MethodsWe propose a 3D depth-wise separable convolution, and construct channel expansional convolution (CEC) unit and inverted residual block (IRB) to reduce the parameter count and computational load, making the network more suitable for use in resource-constrained environments. In addition, an auxiliary classification task (ACT) is introduced in the proposed architecture to provide additional supervisory signals for the main task of segmentation. The network architecture features a contracting path for downsampling and an expanding path for precise localization, enhanced by skip connections that integrate multi-level semantic information.ResultsThe network was evaluated using a dataset of Dynamic Contrast Enhanced MRI (DCE-MRI) breast cancer images, and the results show that compared to the classical 3DU-Net, MM-3DUNet could significantly reduce model parameters by 63.16% and computational demands by 80.90%, while increasing segmentation accuracy by 1.30% in IoU (Intersection over Union).ConclusionsMM-3DUNet offers a substantial reduction in computational requirements of breast cancer mass segmentation network. This network not only enhances diagnostic precision but also supports deployment in diverse clinical settings, potentially improving early detection and treatment outcomes for breast cancer patients. |
| format | Article |
| id | doaj-art-98528c68a9394f87a8db983e1827f53a |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-98528c68a9394f87a8db983e1827f53a2025-08-20T01:50:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-05-011510.3389/fonc.2025.15639591563959MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolutionXian Wang0Wenzhi Zeng1Junzeng Xu2Senhao Zhang3Yuexing Gu4Benhui Li5Xueyang Wang6Attending Physician of Health Management Institute, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaGroup of Agricultural High-Efficiency Water Management and Artificial Intelligence, College of Agricultural Science and Engineering, Hohai University, Nanjing, Jiangsu, ChinaGroup of Agricultural High-Efficiency Water Management and Artificial Intelligence, College of Agricultural Science and Engineering, Hohai University, Nanjing, Jiangsu, ChinaDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Yancheng Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, ChinaDepartment of Radiology, Yancheng Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, ChinaDepartment of Radiology, Yancheng Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, ChinaBackground and objectivesThis paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary classification tasks to enhance feature representation and computational efficiency.MethodsWe propose a 3D depth-wise separable convolution, and construct channel expansional convolution (CEC) unit and inverted residual block (IRB) to reduce the parameter count and computational load, making the network more suitable for use in resource-constrained environments. In addition, an auxiliary classification task (ACT) is introduced in the proposed architecture to provide additional supervisory signals for the main task of segmentation. The network architecture features a contracting path for downsampling and an expanding path for precise localization, enhanced by skip connections that integrate multi-level semantic information.ResultsThe network was evaluated using a dataset of Dynamic Contrast Enhanced MRI (DCE-MRI) breast cancer images, and the results show that compared to the classical 3DU-Net, MM-3DUNet could significantly reduce model parameters by 63.16% and computational demands by 80.90%, while increasing segmentation accuracy by 1.30% in IoU (Intersection over Union).ConclusionsMM-3DUNet offers a substantial reduction in computational requirements of breast cancer mass segmentation network. This network not only enhances diagnostic precision but also supports deployment in diverse clinical settings, potentially improving early detection and treatment outcomes for breast cancer patients.https://www.frontiersin.org/articles/10.3389/fonc.2025.1563959/fullmulti-task mobile 3D UNetdynamic contrast enhanced MRIbreast cancer images segmentationresource-constrained environmentsconvolutional neural networks |
| spellingShingle | Xian Wang Wenzhi Zeng Junzeng Xu Senhao Zhang Yuexing Gu Benhui Li Xueyang Wang MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution Frontiers in Oncology multi-task mobile 3D UNet dynamic contrast enhanced MRI breast cancer images segmentation resource-constrained environments convolutional neural networks |
| title | MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution |
| title_full | MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution |
| title_fullStr | MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution |
| title_full_unstemmed | MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution |
| title_short | MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution |
| title_sort | mm 3d unet development of a lightweight breast cancer tumor segmentation network utilizing multi task and depthwise separable convolution |
| topic | multi-task mobile 3D UNet dynamic contrast enhanced MRI breast cancer images segmentation resource-constrained environments convolutional neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1563959/full |
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