Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images Using a Local Attention-Based Graph Convolution Network.

Autor:	Lang Y; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Lian C; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Xiao D; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Deng H; Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX, USA., Yuan P; Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX, USA., Gateno J; Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX, USA.; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, Ithaca, NY, USA., Shen SGF; Department of Oral and Craniofacial Surgery, Shanghai 9th Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, China., Alfi DM; Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX, USA.; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, Ithaca, NY, USA., Yap PT; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Xia JJ; Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX, USA.; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, Ithaca, NY, USA., Shen D; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Jazyk:	angličtina
Zdroj:	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2020 Oct; Vol. 12264, pp. 817-826. Date of Electronic Publication: 2020 Sep 29.
DOI:	10.1007/978-3-030-59719-1_79
Abstrakt:	Landmark localization is an important step in quantifying craniomaxillofacial (CMF) deformities and designing treatment plans of reconstructive surgery. However, due to the severity of deformities and defects (partially missing anatomy), it is difficult to automatically and accurately localize a large set of landmarks simultaneously. In this work, we propose two cascaded networks for digitizing 60 anatomical CMF landmarks in cone-beam computed tomography (CBCT) images. The first network is a U-Net that outputs heatmaps for landmark locations and landmark features extracted with a local attention mechanism. The second network is a graph convolution network that takes the features extracted by the first network as input and determines whether each landmark exists via binary classification. We evaluated our approach on 50 sets of CBCT scans of patients with CMF deformities and compared them with state-of-the-art methods. The results indicate that our approach can achieve an average detection error of 1.47mm with a false positive rate of 19%, outperforming related methods.
Databáze:	MEDLINE
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