Abstract：
OBJECTIVE: Microvascular decompression (MVD) is a widely used neurosurgical intervention for the treatment of cranial nerves compression. Segmentation of MVD-related structures, including the brainstem, nerves, arteries, and veins, is critical for preoperative planning and intraoperative decision-making. Automatically segmenting structures related to MVD is still challenging for current methods due to the limited information from a single modality and the complex topology of vessels and nerves.
METHODS: Considering that it is hard to distinguish MVD-related structures, especially for nerve and vessels with similar topology, we design a multimodal segmentation network with a shared encoder-dual decoder structure and propose a clinical knowledge-driven distillation scheme, allowing reliable knowledge transferred from each decoder to the other. Besides, we introduce a class-wise contrastive module to learn the discriminative representations by maximizing the distance among classes across modalities. Then, a projected topological loss based on persistent homology is proposed to constrain topological continuity.
RESULTS: We evaluate the performance of our method on in-house dataset consisting of 100 paired HR-T2WI and 3D TOF-MRA volumes. Experiments indicate that our model outperforms the SOTA in DSC by 1.9% for artery, 3.3% for vein and 0.5% for nerve. Visualization results show our method attains improved continuity and less breakage, which is also consistent with intraoperative images.
CONCLUSIONS: Our method can comprehensively extract the distinct features from multimodal data to segment the MVD-related key structures and preserve the topological continuity, allowing surgeons precisely perceiving the patient-specific target anatomy and substantially reducing the workload of surgeons in the preoperative planning stage. Our resources will be publicly available at https://github.com/JaronTu/Multimodal_MVD_Seg .
METHODS: Considering that it is hard to distinguish MVD-related structures, especially for nerve and vessels with similar topology, we design a multimodal segmentation network with a shared encoder-dual decoder structure and propose a clinical knowledge-driven distillation scheme, allowing reliable knowledge transferred from each decoder to the other. Besides, we introduce a class-wise contrastive module to learn the discriminative representations by maximizing the distance among classes across modalities. Then, a projected topological loss based on persistent homology is proposed to constrain topological continuity.
RESULTS: We evaluate the performance of our method on in-house dataset consisting of 100 paired HR-T2WI and 3D TOF-MRA volumes. Experiments indicate that our model outperforms the SOTA in DSC by 1.9% for artery, 3.3% for vein and 0.5% for nerve. Visualization results show our method attains improved continuity and less breakage, which is also consistent with intraoperative images.
CONCLUSIONS: Our method can comprehensively extract the distinct features from multimodal data to segment the MVD-related key structures and preserve the topological continuity, allowing surgeons precisely perceiving the patient-specific target anatomy and substantially reducing the workload of surgeons in the preoperative planning stage. Our resources will be publicly available at https://github.com/JaronTu/Multimodal_MVD_Seg .
摘要：
目的：微血管减压术（MVD）是一种广泛使用的神经外科介入治疗颅神经压迫的方法。MVD相关结构的分割，包括脑干,神经,动脉,和静脉，对于术前计划和术中决策至关重要。由于来自单一模态的有限信息以及血管和神经的复杂拓扑结构,自动分割与MVD相关的结构对于当前方法仍然具有挑战性。
方法：考虑到很难区分与MVD相关的结构，特别是对于具有相似拓扑结构的神经和血管，我们设计了一个具有共享编码器-双解码器结构的多模态分割网络，并提出了一种临床知识驱动的蒸馏方案，允许可靠的知识从每个解码器转移到另一个。此外,我们引入了一个类对比模块，通过最大化跨模态的类之间的距离来学习判别表示。然后，提出了一种基于持续同源性的投影拓扑损失来约束拓扑连续性。
结果：我们在由100个配对的HR-T2WI和3DTOF-MRA卷组成的内部数据集上评估了我们方法的性能。实验表明，对于动脉，我们的模型优于DSC中的SOTA1.9%，静脉为3.3%，神经为0.5%。可视化结果表明，我们的方法提高了连续性，减少了破损，这也与术中图像一致。
结论：我们的方法可以从多模态数据中全面提取不同的特征，以分割与MVD相关的关键结构并保持拓扑连续性，允许外科医生精确感知患者特定的目标解剖结构，并大大减少外科医生在术前计划阶段的工作量。我们的资源将在https://github.com/JaronTu/Multimodal_MVD_Seg上公开。
方法：考虑到很难区分与MVD相关的结构，特别是对于具有相似拓扑结构的神经和血管，我们设计了一个具有共享编码器-双解码器结构的多模态分割网络，并提出了一种临床知识驱动的蒸馏方案，允许可靠的知识从每个解码器转移到另一个。此外,我们引入了一个类对比模块，通过最大化跨模态的类之间的距离来学习判别表示。然后，提出了一种基于持续同源性的投影拓扑损失来约束拓扑连续性。
结果：我们在由100个配对的HR-T2WI和3DTOF-MRA卷组成的内部数据集上评估了我们方法的性能。实验表明，对于动脉，我们的模型优于DSC中的SOTA1.9%，静脉为3.3%，神经为0.5%。可视化结果表明，我们的方法提高了连续性，减少了破损，这也与术中图像一致。
结论：我们的方法可以从多模态数据中全面提取不同的特征，以分割与MVD相关的关键结构并保持拓扑连续性，允许外科医生精确感知患者特定的目标解剖结构，并大大减少外科医生在术前计划阶段的工作量。我们的资源将在https://github.com/JaronTu/Multimodal_MVD_Seg上公开。
