Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexistence with other lesions/hemorrhages, remains unexpl...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-10, Vol.26 (10), p.5165-5176
Main Authors: Hua, Yineng, Yan, Zengqiang, Kuang, Zhuo, Zhang, Hang, Deng, Xianbo, Yu, Li
Format: Article
Language:English
Subjects:
Bioinformatics
Brain
Cerebral ventricle segmentation
Cerebral ventricles
Coexistence
Computer networks
Deep learning
Hemorrhage
Hemorrhaging
Image segmentation
intra-ventricular hemorrhage
Magnetic resonance imaging
Segmentation
Symmetry
Three-dimensional displays
Training
Ventricle
Ventricles (cerebral)
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Summary:Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexistence with other lesions/hemorrhages, remains unexplored. In this paper, we, for the first time, focus on cerebral ventricle segmentation with the presence of intra-ventricular hemorrhages (IVH). To overcome the occlusions formed by IVH, we propose a symmetry-aware deep learning approach inspired by contrastive self-supervised learning. Specifically, for each slice, we jointly employ the raw slice and the horizontally flipped slice as inputs and penalize the consistency loss between the corresponding segmentation maps in addition to their segmentation losses. In this way, the symmetry of cerebral ventricles is enforced to eliminate the occlusions brought by IVH. Extensive experimental results show that the proposed symmetry-aware deep learning approach achieves consistent performance improvements for ventricle segmentation in both normal ( i.e. without IVH) and challenging cases ( i.e. with IVH). Through evaluation of multiple backbone networks, we demonstrate the architecture-independence of the proposed approach for performance improvements. Moreover, we re-design an end-to-end version of symmetry-aware deep learning, making it more extendable to other approaches for brain-related analysis.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3190494