Automatic MRI segmentation of pectoralis major muscle using deep learning

Abstract

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.

The Cross-Sectional Area Assessment of Pelvic Muscles Using the MRI Manual Segmentation among Patients with Low Back Pain and Healthy Subjects

“…Many studies confirming the validity of magnetic resonance image segmentation application of muscle atrophy symptomatic assessment were conducted in patients with LBP [ 13 , 14 , 15 , 16 ]. Most of them focused on the psoas major and multifidus muscle [ 18 , 19 , 21 , 29 , 30 , 31 ]. Moreover, the relationship of low back pain for the decrease in the cross-sectional area of the psoas major muscle and the increase in adipose tissue infiltration was confirmed [ 32 ].…”

A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans

“…When combined with the significant time-savings provided by the automated segmentation in comparison to the human observers, it provides substantial evidence for the use of this pipeline to analyse large cohorts, such as CanCOLD. In addition, our model accuracy was either similar (magnetic resonance imaging-based; DSC: 0.94±0.01) [ 20 ] or slightly higher (CT-based; DSC: 0.93±0.04) [ 19 ] than other state-of-the art pectoralis segmentation networks from the literature. Unlike the previously published work, we also quantified model performance on an external never-before-seen dataset of research study participants with COPD, which is important for testing generalisability.…”

“…Multiple studies have trained such models to automatically extract body composition measurements in abdominal CT with high accuracy [ 17 , 18 ]. However, few studies have developed an automated method to quantify the PMA from chest scans [ 19 , 20 ], and no studies have performed both automatic identification of the aortic arch and pectoralis segmentation.…”

Rhizonet: Image Segmentation for Plant Root in Hydroponic Ecosystem

“…We also integrate MONAI (Medical Open Network for AI)an open-source, PyTorch-based framework tailored for healthcare imaging. MONAI offers specialized functionalities to develop deep learning techniques for biological imaging tasks 25,26 and optimizes the code for rapid processing. Crucially, it facilitates GPU acceleration in our algorithm, significantly boosting the speed of tasks like deep learning-based image segmentation.…”