Home > Archive>Volume 44, Issue 2, 2025 >134-144. DOI:10.13300/j.cnki.hnlkxb.2025.02.014
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Automatic segmentation of muscle fiber and methods of calculating phenotype based on improved Mask-Scoring R-CNN
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Affiliation:

1.College of Mathematics and Informatics，South China Agricultural University，Guangzhou 510642，China;2.National Engineering Research Center for Swine Breeding Industry，Guangzhou 510642，China;3.State Key Laboratory of Swine and Poultry Breeding Industry，Guangzhou 510640，China;4.College of Animal Science，South China Agricultural University，Guangzhou 510642，China

Clc Number:

TP391

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    Abstract:

    A model for instance segmentation based on improved Mask-Scoring R-CNN was proposed and the efficient segmentation of myofibroblast cells was realized to solve the problems of manual and semi-automatic segmentation with accuracy and efficiency and the inadequate performance of general models for segmentation in encountering various interferences of noisy images.The Convolutional Block Attention Module （CBAM） attention mechanism was introduced into the Mask-Scoring R-CNN model to improve the model.The extraction and expression of feature information by the improved model was enhanced to improve the performance of segmentation and the generalization capability of the model in tasks of segmentation.The results of testing the improved Mask-Scoring R-CNN model on a dataset of 103 test images showed that the root mean square error （RMSE） of phenotype measurement value was smaller than that of the original model，with the RMSE of the total number of myofibers decreased from 2.08 to 1.26，the RMSE of area reduced from 212.21 μm2 to 181.36 μm2，and the RMSE of average diameter decreased from 2.87 μm to 1.47 μm.It is indicated that the improved model can effectively deal with noisy images of myofiber and accurately segment each myofiber even in common noisy environments.

    Fig.1 Fiber images of various conditions
    Fig.2 Comparison of two types of muscle fibers in different colors
    Fig.3 Improved CBAM attention mechanism module
    Fig.4 RoI extraction
    Fig.5 Image segmentation based on Mask-Scoring R-CNN
    Fig.6 Calculation of muscle fiber phenotype data
    Fig.8 Segmentation results of muscle fiber cell images using different segmentation models
    Fig.7 The segmentation results of different segmentation models on the image of muscle fiber cells
    Table 1 The performance comparison before and after the improvement of the CBAM module
    Table 2 The performance comparison of different segmentation models
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沃靖杰,田绪红,尹令,杨杰,姚泽锴,蔡更元. Automatic segmentation of muscle fiber and methods of calculating phenotype based on improved Mask-Scoring R-CNN[J]. Jorunal of Huazhong Agricultural University,2025,44(2):134-144.

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  • Received:October 18,2023
  • Online: April 02,2025
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