Ideal Midsagittal Plane Detection Using Deep Hough Plane Network for Brain Surgical Planning

The ideal midsagittal plane (MSP) approximately bisects the human brain into two cerebral hemispheres, and its projection on the cranial surface serves as an important guideline for surgical navigation, which lays a foundation for its significant role in assisting neurosurgeons in planning surgical incisions during preoperative planning. However, the existing plane detection algorithms are generally based on iteration procedure, which have the disadvantages of low efficiency, poor accuracy, and unable to extract the non-local plane features. In this study, we propose an end-to-end deep Hough plane network (DHPN) for ideal MSP detection, which has four highlights. First, we introduce differentiable deep Hough transform (DHT) and inverse deep Hough transform (IDHT) to achieve the mutual transformation between semantic features and Hough features, which converts and simplifies the plane detection problem in the image space into a keypoint detection problem in the Hough space. Second, we design a sparse DHT strategy to increase the sparsity of features, improving inference speed and greatly reducing calculation cost in the voting process. Third, we propose a Hough pyramid attention network (HPAN) to further extract non-local features by aggregating Hough attention modules (HAM). Fourth, we introduce dual space supervision (DSS) mechanism to integrate training loss from both image and Hough spaces. Through extensive validations on a large inhouse dataset, our method outperforms state-of-the-art methods on the ideal MSP detection task.