PEPC-Net: Progressive Edge Perception and Completion Network for Precise Identification of Safe Resection Margins in Maxillofacial Cysts

Maxillofacial cysts pose significant surgical risks due to their proximity to critical anatomical structures, such as blood vessels and nerves. Precise identification of the safe resection margins is essential for complete lesion removal while minimizing damage to surrounding at-risk tissues, which highly relies on accurate segmentation in CT images. However, due to the limited space and complex anatomical structures in the maxillofacial region, along with heterogeneous compositions of bone and soft tissues, accurate segmentation is extremely challenging. Thus, a Progressive Edge Perception and Completion Network (PEPC-Net) is presented in this study, which integrates three novel components: 1) Progressive Edge Perception Branch, which progressively fuses semantic features from multiple resolution levels in a dual-stream manner, enabling the model to handle the varying forms of maxillofacial cysts at different stages. 2) Edge Information Completion Module, which captures subtle, differentiated edge features from adjacent layers within the encoding blocks, providing more comprehensive edge information for identifying heterogeneous boundaries. 3) Edge-Aware Skip Connection to adaptively fuse multi-scale edge features, preserving detailed edge information, to facilitate precise identification of the cyst boundaries. Extensive experiments on clinically collected maxillofacial lesion datasets validate the effectiveness of the proposed PEPC-Net, achieving a DSC of 88.71% and an ASD of 0.489mm. It's generalizability is further assessed using an external validation set, which includes more diverse range of maxillofacial cyst cases and images of varying qualities. These experiments highlight the superior performance of PEPC-Net in delineating the polymorphic edges of heterogeneous lesions, which is critical for safe resection margins decision.