基于半监督学习和条件概率的膝关节软骨MRI图像分割  

MRI Image Segmentation of Knee Cartilage Based on Semi-supervised Learning and Conditional Probability

作　　者：马春帅 程远志 MA Chun-Shuai;CHENG Yuan-Zhi(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2025年第1期100-109,共10页Computer Systems & Applications

基　　金：国家自然科学基金(61806107,61702135)。

摘　　要：本文提出了一种基于半监督学习和条件概率的膝关节软骨分割方法,旨在解决医学图像分割中标注样本数量不足和质量差的问题.现有的标签树嵌入深度学习模型难以对网络输出间的层次关系进行有效建模,而本文提出了一种条件到无条件的混合训练与任务级一致性结合的方法,有效地利用了标签之间的层次关系和相似性,提高了分割精度.具体来说,本文使用一个联合预测像素级分割图和目标的几何感知水平集表示的双任务深度网络.通过可微分的任务变换层,将水平集表示转换为近似的分割映射.同时,本文在标签和未标记数据上引入了基于水平线的分割映射与直接预测的分割映射之间的任务级一致性正则化.在两个公共数据集上的大量实验表明,本文的方法可以通过包含未标记的数据来显著提高性能.This study introduces a knee cartilage segmentation method based on semi-supervised learning and conditional probability,to address the scarcity and quality issues of annotated samples in medical image segmentation.As it is difficult for existing embedded deep learning models to effectively model the hierarchical relationships among network outputs,the study proposes an approach combining conditional-to-unconditional mixed training and task-level consistency.In this way,the hierarchical relationships and relevance among labels are efficiently utilized,and the segmentation accuracy is enhanced.Specifically,the study employs a dual-task deep network predicting both pixel-level segmentation images and geometric perception level set representations of the target.The level set is shifted into an approximate segmentation map through a differentiable task transformation layer.Meanwhile,the study also introduces task-level consistency regularization between level line-based and directly predicted segmentation maps on labeled and unlabeled data.Extensive experiments on two public datasets demonstrate that this approach can significantly improve performance through the incorporation of unlabeled data.

关 键 词：膝关节软骨图像分割 多标签分类 半监督学习 标签相关性 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]