Multi-task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT.
Journal
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Part Of
Conference on Medical Image Computing and Computer-Assisted Intervention
Journal Volume
12264
Start Page
807
End Page
816
Date Issued
2020-10
Author(s)
Lian, Chunfeng
Wang, Fan
Deng, Hannah H
Wang, Li
Xiao, Deqiang
Kuang, Tianshu
Gateno, Jaime
Shen, Steve G F
Yap, Pew-Thian
Xia, James J
Shen, Dinggang
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. , 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.
Subjects
Craniomaxillofacial (CMF)
Landmark localization
Multi-task learning
Segmentation
Type
conference paper