Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

Chen，Xingxing1; Qi，Weizhi2; Xi，Lei2

2019-12-01

10.1186/s42492-019-0022-9

Visual Computing for Industry Biomedicine and Art   影响因子和分区

2096-496X

2524-4442

In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.

Deep learning Motion correction Optical resolution photoacoustic microscopy

EI

英语

通讯

20220411526467

Blood vessels ; Deep learning ; Neural networks

Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Imaging Techniques:746

2-s2.0-85095277833

Scopus

1.School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu,610054,China
2.Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Chen，Xingxing,Qi，Weizhi,Xi，Lei. Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy[J]. Visual Computing for Industry Biomedicine and Art,2019,2(1).

Chen，Xingxing,Qi，Weizhi,&Xi，Lei.(2019).Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy.Visual Computing for Industry Biomedicine and Art,2(1).

Chen，Xingxing,et al."Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy".Visual Computing for Industry Biomedicine and Art 2.1(2019).