Automatic MRI segmentation of pectoralis major muscle using deep learning

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.

Deep learning method for segmentation of rotator cuff muscles on MR images

OBJECTIVE

To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN approach can accurately perform both tasks compared with manual reference standards.

MATERIAL AND METHODS

We created 2 models: model A for Y-view selection and model B for muscle segmentation. For model A, we manually selected shoulder sagittal T1 Y-views from 258 cases as ground truth to train a classification CNN (Keras/Tensorflow, Inception v3, 16 batch, 100 epochs, dropout 0.2, learning rate 0.001, RMSprop). A top-3 success rate evaluated model A on 100 internal and 50 external test cases. For model B, we manually segmented subscapularis, supraspinatus, and infraspinatus/teres minor on 1048 sagittal T1 Y-views. After histogram equalization and data augmentation, the model was trained from scratch (U-Net, 8 batch, 50 epochs, dropout 0.25, learning rate 0.0001, softmax). Dice (F1) score determined segmentation accuracy on 105 internal and 50 external test images.

RESULTS

Model A showed top-3 accuracy > 98% to select an appropriate Y-view. Model B produced accurate RC muscle segmentations with mean Dice scores > 0.93. Individual muscle Dice scores on internal/external datasets were as follows: subscapularis 0.96/0.93, supraspinatus 0.97/0.96, and infraspinatus/teres minor 0.97/0.95.

CONCLUSIONS

Our results show overall accurate Y-view selection and automated RC muscle segmentation using a combination of deep CNN algorithms.

Supervised Classification of the Accuracy of the Time Delay Estimation in Ultrasound Elastography

The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of normalized cross correlation (NCC) at the estimated displacement. NCC value, however, could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. Herein, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay. We extract several features from the NCC profile, and utilize support vector machine to classify peak-hopping and jitter error. The results on simulation, phantom, and in vivo data show the significant improvement of the proposed algorithm compared to the state of the art techniques.

A Novel Elastographic Frame Quality Indicator and its use in Automatic Representative-Frame Selection from a Cine Loop

This study was aimed at developing a method for automatically selecting a few representative frames from several hundred axial-shear strain elastogram frames typically obtained during freehand compression elastography of the breast in vivo. This may also alleviate some inter-observer variations that arise at least partly because of differences in selection of representative frames from a cine loop for evaluation and feature extraction. In addition to the correlation coefficient and frame-average axial strain that have been previously used as quality indicators for axial strain elastograms, we incorporated the angle of compression, which has unique effects on axial-shear strain elastogram interpretation. These identified quality factors were computed for every frame in the elastographic cine loop. The algorithm identifies the section having N contiguous frames (N = 10) that possess the highest cumulative quality scores from the cine loop as the one containing representative frames. Data for total of 40 biopsy-proven malignant or benign breast lesions in vivo were part of this study. The performance of the automated algorithm was evaluated by comparing its selection against that by trained radiologists. The observer- identified frame that consisted of a sonogram, axial strain elastogram and axial-shear strain elastogram was compared with the respective images in the frames of the algorithm-identified section using cross-correlation as a similarity measure. It was observed that there was, on average (∼standard deviation), 82.2% (∼2.2%), 83.4% (∼3.8%) and 78.4% (∼3.6%) correlation between corresponding images of the observer-selected and algorithm-selected frames, respectively. The results indicate that the automatic frame selection method described here may provide an objective way to select a representative frame while saving time for the radiologist. Furthermore, the frame quality metric described and used here can be displayed in real time as feedback to guide elastographic data acquisition and for training purposes.