[1]吴洪柱,李晓琳,彭博,等.BiNETR：基于双流金字塔解码器和深监督的MRI颅骨分割方法[J].中国医学物理学杂志,2025,42(8):1018-1025.[doi:DOI:10.3969/j.issn.1005-202X.2025.08.006]
　WU Hongzhu,LI Xiaolin,PENG Bo,et al.BiNETR: MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision[J].Chinese Journal of Medical Physics,2025,42(8):1018-1025.[doi:DOI:10.3969/j.issn.1005-202X.2025.08.006]
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BiNETR：基于双流金字塔解码器和深监督的MRI颅骨分割方法()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第8期
页码:
1018-1025
栏目:
医学影像物理
出版日期:
2025-08-30

文章信息/Info

Title:
BiNETR: MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision
文章编号:
1005-202X（2025）08-1018-08
作者:
吴洪柱1; 李晓琳2; 彭博2; 周志勇2; 戴亚康1; 2
1.徐州医科大学医学影像学院， 江苏 徐州 221004； 2.中国科学院苏州生物医学工程技术研究所， 江苏 苏州 215163
Author(s):
WU Hongzhu1;  LI Xiaolin2;  PENG Bo2;  ZHOU Zhiyong2;  DAI Yakang1;  2
1. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221004, China 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China
关键词:
颅骨分割; 深度学习; 双流金字塔; 磁共振成像; 深监督
Keywords:
Keywords: skull segmentation deep learning bi-stream pyramid magnetic resonance imaging deep supervision
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2025.08.006
文献标志码:
A
摘要:
磁共振图像颅骨分割为MEG、EEG正问题提供真实的颅骨模型。为解决因MRI颅骨成像模糊、结构复杂导致难以分割的问题，提出基于双流金字塔解码器和深监督的MRI颅骨分割方法。该方法在编-解码的网络结构中以双流金字塔解码器作为主解码器，包括串行的边缘信息引导和精细特征融合双解码器。边缘信息引导金字塔解码器基于特征锐化有效增强边缘信息，提高边缘分割精度。精细特征融合金字塔解码器对边缘增强后特征进一步细化和重用，促进深层、浅层特征的融合。此外，引入深监督计算中间特征损失，从而将梯度植入深层网络，增强网络的训练。分割算法在颅骨数据集进行验证，Dice相似系数为0.880±0.039，平均对称表面距离为（0.931±0.286） mm，性能优于其他先进方法。实验结果表明该算法在MRI颅骨分割任务中的有效性和准确性。
Abstract:
Abstract: Skull segmentation in magnetic resonance image (MRI) provides realistic skull models for MEG and EEG positive problems. An MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision (BiNETR) is proposed to solve the problem of difficult segmentation due to the blurred and complex structure of MRI skull imaging. The method uses a bi-stream pyramid decoder as the main decoder in the network structure of encoding-decoding, including serial dual decoders for edge information oriented and precise feature merging. Specifically, edge information oriented pyramid decoder effectively enhances the edge information based on feature sharpening to improve the edge segmentation accuracy, and the precise feature merging pyramid decoder further refines and reuses the edge-enhanced features to promote the fusion of deep and shallow features. In addition, deep supervised computation of intermediate feature loss is introduced to implant the gradient into the deep network for enhancing network training. The segmentation algorithm is validated on the skull dataset, achieving a Dice similarity coefficient of 0.880±0.039 and an average symmetric surface distance of (0.931±0.286) mm, outperforming other state-of-the-art methods. The experimental results demonstrate the effectiveness and accuracy of the proposed algorithm in MRI skull segmentation.

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备注/Memo

备注/Memo:
【收稿日期】2025-02-15 【基金项目】国家自然科学基金（62471467, 62271481）；苏州市基础研究项目（SJC2022012, SSD2023008）；苏州市重点实验室项目（SZS2024007） 【作者简介】吴洪柱，硕士研究生，研究方向：医学图像处理，E-mail: 302103110602@stu.xzhmu.edu.cn 【通信作者】周志勇，研究员，博士生导师，博士，研究方向：医学图像处理，E-mail: zhouzy@sibet.ac.cn；戴亚康，研究员，博士生导师，博士，研究方向：医学图像处理，E-mail: daiyk@sibet.ac.cn（周志勇与戴亚康为共同通信作者）
更新日期/Last Update: 2025-09-15