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 seg-mentation can help diagnose brain-related diseases by providing valuable auxiliary information.However,manual delinea-tion 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 charac-terized by their high contrast and clear boundaries.The main challenge lies in the segmentation of small-scale cerebral ven-tricles in some slices.Notably,in healthy/normal cases,cerebral ventricles are not perfectly symmetric;therefore,penal-izing a symmetry constraint would be helpful,especially in dealing with the low-contrast small-scale regions.Cerebral ven-tricle 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 ven-tricles 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.,cere-bral 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 ven-tricle segmentation.Considering the large hemorrhages,the hemorrhages not only cover parts of the cerebral ventricles but also several background regions.All the regions share similar appearances and contrasts.Classifying these regions as back-ground would produce numerous false negatives,while segmenting them as cerebral ventricles would generate quite a few false positives.Therefore,large-scale IVH would poorly affect cerebral ventricle segmentation performance.Based on the above analysis,this study focuses on the cerebral ventricle segmentation problems of patients with IVH and proposes tar-geted ventricle segmentation methods for the problems of target occlusion and unclear boundaries.The core idea of the pro-posed framework is to utilize the symmetry of cerebral ventricles as guidance to alleviate the occlusions formed by IVH.Thus,an end-to-end contrastive learning-based symmetry-aware ventricle segmentation network is proposed in the paper.The model first implements adaptive image correction based on spatial transform networks without additional annotations to obtain the ventricle symmetric images of the input images at any position.A symmetry-aware learning loop is then con-structed.The symmetric image pairs are simultaneously inputted into the segmentation network.The ventricles predicted by the network are forced to be symmetric by emphasizing the similarity of the segmentation result pairs.Thus,the occlu-sions formed by IVH could be alleviated by referring to the healthy ventricles.The ventricles are not completely symmetric;thus,pursuing"hard"symmetry during training is infeasible.Therefore,the contrastive learning algorithm is further com-bined with the weighted symmetry loss function to impose symmetry constraints on the images.The network can be trained end-to-end,enabling the upstream network to collaborate with the downstream segmentation task.Result Experimental results based on different segmentation network models demonstrate that the approach proposed in this paper can achieve consistent performance improvements in multiple evaluation metrics in the ventricle segmentation task of patients with IVH.The average increase in patient-and slice-wise dice coefficients based on different baseline models when introducing the proposed method is 1.09％and 1.28％,respectively.When evaluated on the patient level,the optimal model achieved a Dice coefficient and recall of 85.17％and 84.03％,respectively,by incorporating the algorithm proposed in this paper.The qualitative results also reveal the superior performance of the proposed algorithm,which achieves smooth boundaries and complete ventricles with fewer false positives.Conclusion This paper focuses on cerebral ventricle segmentation,espe-cially with the existence of IVH.Compared to cerebral ventricle segmentation in healthy/normal cases,occlusions formed by IVH would make it challenging for segmentation.Based on the symmetry of cerebral ventricles,a symmetry-aware approach combined with contrastive self-supervised learning is introduced.Therefore,the occlusions are effectively allevi-ated by referring to the healthy/normal parts of the cerebral ventricles.Experimental results on two different datasets demon-strate a notable advancement in ventricle segmentation of computed tomography(CT)and magnetic resonance(MR)images of healthy/normal and IVH cases.Moreover,the IVH cases demonstrate considerable improvement.More importantly,the pro-posed approach is independent of specific deep learning architectures and introduces no additional computational complexity.Therefore,the method presented in this paper has strong portability and can be applied to various segmentation networks.
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