An efficient two-step multi-organ registration on abdominal CT via deep-learning based segmentation. (September 2021)

• Record Type: Journal Article
• Authors: Yang, Shao-di; Zhao, Yu-qian; Zhang, Fan; Liao, Miao; Yang, Zhen; Wang, Yan-jin; Yu, Ling-li
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Non-rigid registration -- Deep learning -- ROI segmentation -- Abdominal CT; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103027
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: An automatic ROI-based abdominal CT registration framework is proposed. A FCN-based abdominal CT multi-organ segmentation model is proposed. A robust non-rigid method is developed for multi-organ ROIs registration. It outperforms some existing methods on abdominal CT ROIs registration. Abstract: Registration of three dimensional (3D) abdominal computed tomography (CT) scans is essential for computer-aided disease diagnosis and treatment, but the non-rigid respiratory movements of abdomen increase its difficulty. An automatic two-step multi-organ registration method is presented in this paper for abdominal CT scans. First, the lightweight squeeze-and-excitation (SE) attention blocks and the fully connected conditional random field (CRF)-based post-processing are integrated into a fully convolutional networks (FCN) based model, which can achieve more accurate segmentation results for abdominal multiple organs such as liver, kidneys, and spleen. Then, a non-rigid local correlation coefficient (LCC) similarity metric and an isotropic total variation regularization are combined to register the multi-organ regions, which can reduce computation time and avoid the over-smooth problem of deformation field. The proposed method is validated on three public abdominal CT databases, and the experimental results show that the segmentation and registration performances of our method outperform those of some competing methods. Moreover, compared with global registration strategy, the average registration time of the proposed method is shortened by 20.45%, and the average MSE, PSNR, SSIM, and DICE values are also improved by 7.21%, 22.37%, 15.80%, and 14.43%, respectively.