端到端对称感知对比学习脑室分割算法  

End-to-end symmetry-aware-based contrastive learning cerebral ventricle segmentation algorithm

作　　者：喻莉 华毅能 Yu Li;Hua Yineng(School of Electronic Information and Communication,Huazhong University of Science and Technology,Wuhan 430070,China)

机构地区：[1]华中科技大学电子信息与通信学院,武汉430070

出　　处：《中国图象图形学报》2024年第11期3433-3446,共14页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62271220);湖北省自然科学基金(2024AFB659)。

摘　　要：目的脑室是人脑重要结构,在临床实践中,其大小、形状变化与多种慢性和急性神经系统疾病息息相关,对脑室的精确分割能够为脑部相关疾病的诊断提供有价值的辅助信息。随着深度学习在医学图像处理领域的迅速发展,医学图像分割任务取得了重大进展。然而,脑室内出血患者的脑室分割问题仍然有待探索。方法本文聚焦于脑室内出血患者的脑室分割问题,针对其面临的目标遮挡、边界不清晰等问题,提出针对性的脑室分割算法——基于端到端对比学习对称感知的脑室分割网络。该模型首先基于空间转换网络实现自适应图像校正,获取任意角度下输入图像的脑室对称图像。然后通过对比学习算法并结合加权对称损失函数施加对图像的对称性约束。通过上述方法可实现脑室分割网络的端到端训练,上游网络与下游分割任务协同合作。结果基于不同分割网络模型的实验结果表明,该方案在脑室内出血患者的脑室分割任务上可取得性能提升,该方案按病例和切片评估的Dice系数指标平均增益分别达到1.09%和1.28%。结合本文算法,最优模型按病例评估的DSC(Dice similarity coefficient)系数和召回率分别达到85.17%和84.03%。结论本文所提出算法对CT(computed tomography)和MR(magnetic resonance)图像的脑室分割均取得了有效提升,对脑室内出血患者相关医学图像分割提升尤为显著,并且本文方法可移植性强,可适用于多种分割网络。Objective Cerebral ventricles are one of the most prominent cerebral structures.The size and shape changes of the cerebral ventricle are closely associated with diverse acute and chronic neurological diseases.Accurate ventricle segmentation can help diagnose brain-related diseases by providing valuable auxiliary information.However,manual delineation of cerebral ventricles is a time-consuming task;thus,automatic ventricle segmentation is necessary.Fortunately,with the rapid development of deep learning in the field of medical image processing,automatic medical image segmentation has made considerable progress.However,the ventricle segmentation in patients with intraventricular hemorrhage(IVH)remains unexplored.A few studies focus on the ventricle segmentation of patients with IVH.Method Cerebral ventricle segmentation can be categorized into healthy/normal and IVH cases.Cerebral ventricles in healthy/normal cases are characterized by their high contrast and clear boundaries.The main challenge lies in the segmentation of small-scale cerebral ventricles in some slices.Notably,in healthy/normal cases,cerebral ventricles are not perfectly symmetric;therefore,penalizing a symmetry constraint would be helpful,especially in dealing with the low-contrast small-scale regions.Cerebral ventricle segmentation in healthy/normal cases is generally less challenging.According to the sizes of IVH,the IVH cases are further classified into small-and large-scale IVH cases.For the small-scale IVH cases,though parts of the cerebral ventricles are completely filled by hemorrhages,only the boundary regions would be affected during segmentation.In these cases,the IVH problem would not significantly degrade the segmentation performance because those regions(i.e.,cerebral ventricles filled by IVH)are of high contrast compared to the background,and segmenting the high-contrast regions is roughly equal to cerebral ventricle segmentation.Large-scale IVH cases are the most challenging problem in cerebral ventricle segmentation.Considering the

关 键 词：脑室分割 深度学习 脑室内出血(IVH) 对称感知 端到端网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]