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

Abstract:

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.

journal_name

Skeletal Radiol

journal_title

Skeletal radiology

authors

Medina G,Buckless CG,Thomasson E,Oh LS,Torriani M

doi

10.1007/s00256-020-03599-2

subject

Has Abstract

pub_date

2020-09-16 00:00:00

eissn

0364-2348

issn

1432-2161

pii

10.1007/s00256-020-03599-2

pub_type

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