IEEE J Biomed Health Inform. 2022 Oct;26(10):5165-5176. doi: 10.1109/JBHI.2022.3190494. Epub 2022 Oct 4.
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.
脑室内是大脑的一个重要结构，对其进行分割可以为脑相关疾病的诊断提供丰富的信息。不幸的是，在复杂的临床情况下（如合并其他病变/出血），脑室内的分割仍然没有得到探索。在本文中，我们首次专注于存在脑室内出血（IVH）的脑室内分割。为了克服 IVH 形成的闭塞，我们提出了一种基于对比自监督学习的对称感知深度学习方法。具体来说，对于每一层，我们联合使用原始切片和水平翻转的切片作为输入，并在分割损失之外惩罚对应分割图之间的一致性损失。通过这种方式，强制了脑室内的对称性，以消除 IVH 带来的闭塞。广泛的实验结果表明，所提出的对称感知深度学习方法在正常（即无 IVH）和具有挑战性的情况下（即有 IVH）的心室分割中都能实现一致的性能提升。通过对多个骨干网络的评估，我们证明了所提出的方法在性能提升方面具有架构独立性。此外，我们重新设计了一个端到端的对称感知深度学习版本，使其更易于扩展到其他脑相关分析方法。
