A neural network approach to analyze cross-sections of muscle fibers in pathological images. (January 2019)

• Record Type: Journal Article
• Authors: Li, Ye; Yang, Zhong; Wang, Yaming; Cao, Xinhua; Xu, Xiaoyin
• Is Part Of: Computers in biology and medicine. Volume 104(2019)
• Page Start: 97
• Page End: 104
• Publication Date: 2019-01
• Subjects: Computer-aided analysis -- Segmentation -- Neural network -- Muscle fibers -- Cross-sections -- Microscopic images; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.11.007
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Morphological characteristics of muscle fibers, such as their cross-sections, are important indicators of the health and function of the musculoskeletal system. However, manual analysis of muscle fiber morphology is a labor-intensive and time-consuming process that is prone to errors. Overall, the procedure involves high inter- and intra-observer variability. Therefore, it is desirable for biologists to have a tool that can produce objective and reproducible analysis for muscle fiber images. In this work, we propose a deep convolutional neural network (DCNN) followed by post-processing for detecting and measuring the cross-sections of muscle fibers. We evaluate three segmentation networks for muscle boundary segmentation: (1) U-net, (2) FusionNet, and (3) a customized FusionNet. The customized FusionNet, which had the highest Dice coefficient on the test set, was used for subsequent morphological analysis of the muscle fibers. The proposed method was tested on microscopic images of the tibialis anterior muscles of a pre-clinical model of muscular dystrophy. The dataset contained four mosaic images, totalling more than 3400 fibers. Because of the severity of muscle injury in this pre-clinical model, its muscle fiber images present a challenge for quantitative analysis for several reasons. First, the muscle fibers had inhomogeneous spatial distribution and very different sizes. Second, the membranes of the muscle fibers had uneven signal intensity due to the loss of a membrane protein. Third, the shapes of intact muscle fibers were very different. All these factors contributed to the difficulty of acquiring good training data in the first place. Despite these difficulties, we achieved an average muscle fiber overlay precision of 0.65 and an average recall of 0.49. In this context, overlaid fibers are defined as fibers that have one or more pixels overlaying in the manual and DCNN cross-section segmentation. For the overlaid fibers, the proposed method achieved excellent segmentation accuracy of 94% ± 10.26%, as measured by the Dice-Sorensen coefficient. Highlights: We presented a deep learning approach tailored to analyzing microscopic images of cross sections of muscle fiber. The deep learning neural network was trained on a preclinical model of severe muscular dystrophy. Challenges include inhomogeneous signal intensity, large variation in sizes and uneven distribution of the muscle fibers. Data augmentation was applied to improve the performance of neural network. Results show that the approach can identify the cross sections of intact muscle fibers and avoid diseased muscle fibers.