Automatic location scheme of anatomical landmarks in 3D head MRI based on the scale attention hourglass network. (February 2022)

• Record Type: Journal Article
• Authors: Li, Sai; Gong, Qiong; Li, Haojiang; Chen, Shuchao; Liu, Yifei; Ruan, Guangying; Zhu, Lin; Liu, Lizhi; Chen, Hongbo
• Is Part Of: Computer methods and programs in biomedicine. Volume 214(2022)
• Publication Date: 2022-02
• Subjects: Anatomical landmark -- Medical image -- Scale attention hourglass network -- Full convolutional neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106564
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A scale attention hourglass network was proposedfor landmark location, which can overcome the difficultiesof similar anatomical structures and different image parameters. A two-step automatic location scheme of anatomical landmarks in 3D medical imagewas designed to obtain the coordinates of anatomical landmarks from 3D medical images. Atransfer learning scheme is recruited to improve the efficiency of network training and reduce the amount of training data. A semiautomatic delineation system is designedfor anatomical structure pointsin3D medical imageto reduce the workload of landmark delineation in medical image. Abstract: Background and Objective: An anatomical landmark is biologically meaningful point in medical images and often used for medical image registration. The purpose of this study is to automatically locate anatomical landmarks from 3D medical images. Methods: A two-step automatic location scheme of anatomical landmarks in 3D medical image was designed in this study. In the first step, the full convolutional neural network was used for slice detection from a 3D medical image. In the second step, the scale attention hourglass network was used for landmark location in the detected slice and could overcome the difficulty of similar anatomical structures and different image parameters. This method was implemented and tested on four stable anatomical landmarks in 3D head MRI. Results: A total of 500 and 300 3D head volumes were used for training and testing, respectively. Results showed that the slice detection accuracy reached 85.7% and that the maximum location error was less than one slice. The average accuracy of the four anatomical landmarks in the detected slice reached 87.2%, and the spatial distance was 2.4 ± 2.4, which obtained better performance compared with hourglass network and feature pyramid networks. Conclusions: This method can be useful for locating anatomical landmarks in 3D head MRI and provides technical support for medical image registration and big data analysis.