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.