In orthognathic surgery, accurate segmentation of the pterygopalatine and mandibular canals in maxillofacial cone beam computed tomography (CBCT) scans is crucial. It provides critical information to prevent nerve damage during surgery and significantly reduces the risk of surgical complications. However, the high cost of data collection, strict patient privacy protection, and ethical constraints have hampered the performance of existing deep learning methods for pterygopalatine and mandibular canals segmentation, limiting their practical applicability in clinical settings. To address this challenge and advance the development of pterygopalatine and mandibular canal segmentation techniques in maxillofacial CBCT scans, we carefully constructed and made publicly available a large dataset for pterygopalatine and mandibular canal segmentation in maxillofacial CBCT scans. This dataset includes 191 patient cases and comprehensively covers the key anatomical structures of the maxillary pterygopalatine canal and the mandibular canal, both of which are crucial in orthognathic surgery. Notably, this dataset is the first to include data on the maxillary pterygopalatine canal, filling a significant gap in this field. The release of this dataset will greatly accelerate the development of deep learning-based segmentation methods, provide clinicians with more accurate reconstruction tools, and ultimately improve the safety and efficiency of surgical procedures.
Li et al. (Thu,) studied this question.
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