Multi-task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT

Chunfeng Lian, Fan Wang, Hannah H. Deng, Li Wang, Deqiang Xiao, Tianshu Kuang, Hung Ying Lin, Jaime Gateno, Steve G.F. Shen, Pew Thian Yap, James J. Xia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020
Subtitle of host publication23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages807-816
Number of pages10
Volume12264
ISBN (Electronic)978-3-030-59719-1
ISBN (Print)978-3-030-59718-4
DOIs
StatePublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/8/20

Keywords

  • Craniomaxillofacial (CMF)
  • Landmark localization
  • Multi-task learning
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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