Deep learning method for segmentation of rotator cuff muscles on MR images
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SCIENTIFIC ARTICLE
Deep learning method for segmentation of rotator cuff muscles on MR images Giovanna Medina 1 & Colleen G. Buckless 2 & Eamon Thomasson 2 & Luke S. Oh 1 & Martin Torriani 2 Received: 15 June 2020 / Revised: 27 August 2020 / Accepted: 3 September 2020 # ISS 2020
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. Keywords Shoulder . Muscles . Atrophy . MRI . Artificial intelligence . Segmentation . Rotator cuff
Introduction Rotator cuff (RC) tendon tears are associated with varied degrees of muscle atrophy, manifested by decreased muscle bulk and fatty infiltration [1, 2]. Atrophy of RC musculature is linked to higher rates of repair failure and overall worse clinical outcomes [3–6]. MRI is the reference standard for imaging RC tendons for tears, severity of cuff abnormalities, and postoperative healing [7, 8]. Further, MRI is the Giovanna Medina and Colleen G. Buckless contributed equally to this work. * Martin Torriani [email protected] 1
Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2
Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street – YAW 6048, Boston, MA 02114, USA
preferred method to evaluate RC muscles, enabling quantification and longitudinal assessment of atrophy [2, 9, 10]. Fatty infiltration and degree of atrophy of the supraspinatus muscle have received most attention in studies correlating
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