@inproceedings{d223866d759147b18904f4ac128dc510,
title = "Multi-task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT",
abstract = "Accurate 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. First, 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. Second, 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. Third, adaptive transformer modules (ATMs) are designed for the flexible learning of task-specific feature embedding from common feature bases.",
keywords = "Craniomaxillofacial (CMF), Landmark localization, Multi-task learning, Segmentation",
author = "Chunfeng Lian and Fan Wang and Deng, {Hannah H.} and Li Wang and Deqiang Xiao and Tianshu Kuang and Lin, {Hung Ying} and Jaime Gateno and Shen, {Steve G.F.} and Yap, {Pew Thian} and Xia, {James J.} and Dinggang Shen",
note = "Funding Information: Acknowledgements. This work was supported in part by NIH grants (R01 DE022676, R01 DE027251 and R01 DE021863). Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59719-1_78",
language = "English (US)",
isbn = "978-3-030-59718-4",
volume = "12264",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "807--816",
editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020",
address = "Germany",
}