Automatic MRI segmentation of pectoralis major muscle using deep learning

• Published in: Scientific reports Vol. 12; no. 1; p. 5300
• Main Authors: Godoy, Ivan Rodrigues Barros, Silva, Raian Portela, Rodrigues, Tatiane Cantarelli, Skaf, Abdalla Youssef, de Castro Pochini, Alberto, Yamada, André Fukunishi
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 29.03.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Deep Learning; Image processing; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neural networks; Neural Networks, Computer; Pectoralis Muscles - diagnostic imaging; Segmentation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-09280-z

To develop and validate a deep convolutional neural network (CNN) method capable of selecting the greatest Pectoralis Major Cross-Sectional Area (PMM-CSA) and automatically segmenting PMM on an axial Magnetic Resonance Imaging (MRI). We hypothesized a CNN technique can accurately perform both tasks compared with manual reference standards. Our method is based on two steps: (A) segmentation model, (B) PMM-CSA selection. In step A, we manually segmented the PMM on 134 axial T1-weighted PM MRIs. The segmentation model was trained from scratch (MONAI/Pytorch SegResNet, 4 mini-batch, 1000 epochs, dropout 0.20, Adam, learning rate 0.0005, cosine annealing, softmax). Mean-dice score determined the segmentation score on 8 internal axial T1-weighted PM MRIs. In step B, we used the OpenCV2 (version 4.5.1, https://opencv.org ) framework to calculate the PMM-CSA of the model predictions and ground truth. Then, we selected the top-3 slices with the largest cross-sectional area and compared them with the ground truth. If one of the selected was in the top-3 from the ground truth, then we considered it to be a success. A top-3 accuracy evaluated this method on 8 axial T1-weighted PM MRIs internal test cases. The segmentation model (Step A) produced an accurate pectoralis muscle segmentation with a Mean Dice score of 0.94 ± 0.01. The results of Step B showed top-3 accuracy > 98% to select an appropriate axial image with the greatest PMM-CSA. Our results show an overall accurate selection of PMM-CSA and automated PM muscle segmentation using a combination of deep CNN algorithms.