采用分割一切模型的弱监督互学习三维医学图像配准方法

苏渝钦; 潘杭镇; 李锦江; 刘欢; 李钟毓; 刘杨

doi:10.7652/xjtuxb202608019

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采用分割一切模型的弱监督互学习三维医学图像配准方法

更新时间：2026-07-28

- 采用分割一切模型的弱监督互学习三维医学图像配准方法
- Segment Anything Model Guided Weakly-Supervised Mutual Learning for 3D Medical Image Registration
- 西安交通大学学报 2026年60卷第8期页码：218-228
- 作者机构：
  
  西安交通大学软件学院，710049，西安
  西安交通大学计算机科学与技术学院，710049，西安
  上海交通大学计算机学院，200032，上海
  西安交通大学网络空间安全学院，710049，西安
  西安交通大学智能网络与网络安全教育部重点实验室，710049，西安
- 作者简介：
  
  作者简介：苏渝钦（2002-），男，硕士生；
  刘杨（通信作者），男，副教授。
- 基金信息：
  
  国家自然科学基金资助项目(62202367;62577042)
- DOI：10.7652/xjtuxb202608019
  中图分类号： TP391.41
- 收稿：2025-11-17，
  
  网络首发：2026-04-21，
  
  纸质出版：2026-08-10
- 稿件说明：
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苏渝钦, 潘杭镇, 李锦江, 等. 采用分割一切模型的弱监督互学习三维医学图像配准方法[J]. 西安交通大学学报, 2026,60(8):218-228.

SU Yuqin, PAN Hangzhen, LI Jinjiang, et al. Segment Anything Model Guided Weakly-Supervised Mutual Learning for 3D Medical Image Registration[J]. Journal of Xi'an Jiaotong University, 2026, 60(8): 218-228.
苏渝钦, 潘杭镇, 李锦江, 等. 采用分割一切模型的弱监督互学习三维医学图像配准方法[J]. 西安交通大学学报, 2026,60(8):218-228. DOI： 10.7652/xjtuxb202608019.

SU Yuqin, PAN Hangzhen, LI Jinjiang, et al. Segment Anything Model Guided Weakly-Supervised Mutual Learning for 3D Medical Image Registration[J]. Journal of Xi'an Jiaotong University, 2026, 60(8): 218-228. DOI： 10.7652/xjtuxb202608019.

摘要

针对三维医学图像配准中无监督方法配准精度不足、弱监督方法依赖高质量分割标签且分割基础模型生成标签难以直接稳定指导配准训练的问题，提出了一种采用分割一切模型的弱监督互学习三维医学图像配准方法。首先，利用分割一切模型（SAM）从多视角提取主要解剖区域掩码，并通过融合构建交集掩码与并集掩码，以表征不同粒度的结构先验信息；其次，在弱监督配准框架中引入互学习方法，通过结合可信度与位置信息模块，促进不同掩码监督下配准模型之间的知识蒸馏与协同优化，从而实现互补信息的有效利用并提升训练稳定性。在3组脑图像数据集上的实验结果表明，所提方法能够在充分利用标签信息的同时保持稳定训练，与现有三维图像配准方法相比，在区域重叠度等指标上表现更优，其中重叠系数提升约5%，体现出更高的配准精度与可靠性。

Abstract

To address the problems of insufficient registration accuracy of unsupervised methods

heavy reliance of weakly supervised methods on high-quality segmentation labels

and inability of labels generated by foundation segmentation models to directly and stably guide registration training in 3D medical image registration

a segment anything model (SAM) -guided weakly supervised mutual learning method for 3D medical image registration was proposed. First

masks of major anatomical regions were extracted from different views using the SAM

and intersection and union masks were constructed through mask fusion to represent structural prior information at different granularities. Second

a mutual-learning mechanism was introduced into the weakly supervised registration framework

and a module integrating confidence and position information was embedded to promote knowledge distillation and collaborative optimization between registration models under different mask supervision

thereby enabling effective utilization of complementary information and improving training stability. Finally

experimental results on three brain image datasets demonstrate that the proposed method can fully utilize label information while maintaining stable training

and outperforms existing 3D image registration methods in terms of regional overlap

with the Dice coefficient improved by approximately 5%

indicating higher registration accuracy and reliability.

关键词

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references

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