Evidence map›Paper›PMID 32939590›Full record

ArticleSkeletal radiology2021

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

Giovanna Medina, Colleen G Buckless, Eamon Thomasson, Luke S Oh, Martin Torriani

Abstract read
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Article in Skeletal radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Giovanna Medina *Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Colleen G Buckless *Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6048, Boston, MA, 02114, USA.
Eamon ThomassonDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6048, Boston, MA, 02114, USA.
Luke S OhDepartment of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Martin TorrianiDivision of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6048, Boston, MA, 02114, USA. mtorriani@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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

methodsWe 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.

resultsModel 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.

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

Indexed as

Deep LearningRotator CuffHumansNeural Networks, ComputerShoulderArtificial intelligenceAtrophyMRIMusclesRotator cuffSegmentationShoulder

Identifiers

PMID32939590

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.