Radiomics and Artificial Intelligence Study of Masseter Muscle Segmentation in Patients With Hemifacial Microsomia. Issue 2 (9th March 2023)

• Record Type: Journal Article
• Authors: Han, Wenqing; Xia, Wenjin; Zhang, Ziwei; Kim, Byeong Seop; Chen, Xiaojun; Yan, Yingjie; Sun, Mengzhe; Lin, Li; Xu, Haisong; Chai, Gang; Wang, Lisheng
• Is Part Of: Journal of craniofacial surgery. Volume 34:Issue 2(2023)
• Page Start: 809
• Page End: 812
• Publication Date: 2023-03-09
• Subjects: Hemifacial microsomia -- masseter muscle -- U-net; Facial bones -- Surgery -- Periodicals; Skull -- Surgery -- Periodicals; Face -- Surgery -- Periodicals; Surgery, Plastic -- Periodicals; 617.52
• DOI: 10.1097/SCS.0000000000009105
• Languages: English
• ISSNs: 1049-2275

Abstract

Abstract : Background: Hemifacial microsomia (HFM) is one of the most common congenital craniofacial condition often accompanied by masseter muscle involvement. U-Net neural convolution network for masseter segmentation is expected to achieve an efficient evaluation of masseter muscle. Methods: A database was established with 108 patients with HFM from June 2012 to June 2019 in our center. Demographic data, OMENS classification, and 1-mm layer thick 3-dimensional computed tomography were included. Two radiologists manually segmented masseter muscles in a consensus reading as the ground truth. A test set of 20 cases was duplicated into 2 groups: an experimental group with the intelligent algorithm and a control group with manual segmentation. The U-net follows the design of 3D RoI-Aware U-Net with overlapping window strategy and references to our previous study of masseter segmentation in a healthy population system. Sorensen dice-similarity coefficient (DSC) muscle volume, average surface distance, recall, and time were used to validate compared with the ground truth. Results: The mean DSC value of 0.794±0.028 for the experiment group was compared with the manual segmentation (0.885±0.118) with α=0.05 and a noninferiority margin of 15%. In addition, higher DSC was reported in patients with milder mandible deformity ( r =0.824, P <0.05). Moreover, intelligent automatic segmentation takes only 6.4 seconds showing great efficiency. Conclusions: We first proposed a U-net neural convolutional network and achieved automatic segmentation of masseter muscles in patients with HFM. It is a great attempt at intelligent diagnosis and evaluation of craniofacial diseases.