Abstract
ABSTRACTIntraoperative ultrasound (iUS) has been widely used in recent years to track intraoperative brain tissue deformation. Outlining tumor boundaries on iUS not only facilitates the robustness and accuracy of brain shift correction but also enables the direct use of iUS information for neurosurgical navigation. We developed a semisupervised cross nnU‐Net with depthwise separable convolution (SSC nnSU‐Net) for real‐time segmentation of 3D iUS images by two networks with different initialization but consistent network structure networks. Unlike previous methods, RESECT as labeled data and ReMIND as unlabeled data for hybrid dataset training selected break down the barriers between different datasets and further alleviate the problem of “data hunger.” The SSC nnSU‐Net method was evaluated by ablation of semisupervised learning, comparison with other state‐of‐the‐art methods, and model complexity. The results indicate that the proposed framework achieves a certain balance in terms of computation time, GPU memory utilization, and segmentation performance. This motivates segmentation of 3D iUS images for real‐time application in clinical surgery. The method can assist surgeons in identifying brain tumors through iUS.
Published Version
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