Lian, ChunfengChunfengLianWang, FanFanWangDeng, Hannah HHannah HDengWang, LiLiWangXiao, DeqiangDeqiangXiaoKuang, TianshuTianshuKuangHUNG-YING LINGateno, JaimeJaimeGatenoShen, Steve G FSteve G FShenYap, Pew-ThianPew-ThianYapXia, James JJames JXiaShen, DinggangDinggangShen2025-05-232025-05-232020-10https://scholars.lib.ntu.edu.tw/handle/123456789/729674Accurate bone segmentation and anatomical landmark localization are essential tasks in computer-aided surgical simulation for patients with craniomaxillofacial (CMF) deformities. To leverage the complementarity between the two tasks, we propose an efficient end-to-end deep network, i.e., multi-task dynamic transformer network (DTNet), to concurrently segment CMF bones and localize large-scale landmarks in one-pass from large volumes of cone-beam computed tomography (CBCT) data. Our DTNet was evaluated quantitatively using CBCTs of patients with CMF deformities. The results demonstrated that our method outperforms the other state-of-the-art methods in both tasks of the bony segmentation and the landmark digitization. Our DTNet features three main technical contributions. , a collaborative two-branch architecture is designed to efficiently capture both fine-grained image details and complete global context for high-resolution volume-to-volume prediction. , leveraging anatomical dependencies between landmarks, regionalized dynamic learners (RDLs) are designed in the concept of "learns to learn" to jointly regress large-scale 3D heatmaps of all landmarks under limited computational costs. , adaptive transformer modules (ATMs) are designed for the flexible learning of task-specific feature embedding from common feature bases.enCraniomaxillofacial (CMF)Landmark localizationMulti-task learningSegmentationconference paper10.1007/978-3-030-59719-1_7834935006