Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images

作者全名："Li, Pengcheng; Liu, Yang; Cui, Zhiming; Yang, Feng; Zhao, Yue; Lian, Chunfeng; Gao, Chenqiang"

作者地址："[Li, Pengcheng; Yang, Feng; Zhao, Yue; Gao, Chenqiang] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China; [Li, Pengcheng; Yang, Feng; Zhao, Yue; Gao, Chenqiang] Chongqing Key Lab Signal & Informat Proc, Chongqing 400065, Peoples R China; [Liu, Yang] Chongqing Med Univ, Dept Orthodont, Stomatol Hosp, Chongqing 401147, Peoples R China; [Liu, Yang] Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing 401147, Peoples R China; [Cui, Zhiming] ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China; [Lian, Chunfeng] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China"

通信作者："Zhao, Y (通讯作者)，Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China.; Zhao, Y (通讯作者)，Chongqing Key Lab Signal & Informat Proc, Chongqing 400065, Peoples R China.; Lian, CF (通讯作者)，Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China."

来源：IEEE TRANSACTIONS ON MEDICAL IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000876061700012

JCR分区：Q1

影响因子：10.6

年份：2022

卷号：41

期号：11

开始页：3116

结束页：3127

文献类型：Article

关键词：Teeth; Image segmentation; Dentistry; Semantics; Task analysis; Shape; Knowledge based systems; Tooth segmentation; semantic graph attention; anatomical association modeling; CBCT images

摘要："Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches."

基金机构："Chongqing University of Posts and Telecommunications; Innovative Talents Project [BYJS202105]; Science and Technology Research Program of Chongqing Municipal Education Commission [KJZD-KJQN202000647, KJQN202100646]; National Natural Science Foundation of China [82101058, 62101431, 61906025, 62176035]; Intelligent Medicine Research Project of Chongqing Medical University [ZHYX202101]"

基金资助正文："This work was supported in part by the Chongqing University of Posts and Telecommunications Ph.D. Innovative Talents Project under Grant BYJS202105; in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJZD-KJQN202000647 and Grant KJQN202100646; in part by the National Natural Science Foundation of China under Grant 82101058, Grant 62101431, Grant 61906025, and Grant 62176035; and in part by the Intelligent Medicine Research Project of Chongqing Medical University under Grant ZHYX202101."