Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Autor: Alain Farron, Fabio Becce, Stacey Gidoin, Oskar Truffer, Elham Taghizadeh, Alexandre Terrier, Sylvain Eminian, Philippe Büchler
Jazyk: angličtina
Rok vydání: 2021
Předmět:
muscle atrophy
medicine.medical_specialty
Shoulder
medicine.medical_treatment
computed-tomography
610 Medicine & health
030218 nuclear medicine & medical imaging
Rotator Cuff Injuries
sarcopenia
03 medical and health sciences
0302 clinical medicine
Atrophy
atrophy
Adipose Tissue/diagnostic imaging
Adipose Tissue/pathology
Deep Learning
Humans
Muscular Atrophy/diagnostic imaging
Muscular Atrophy/pathology
Retrospective Studies
Rotator Cuff/diagnostic imaging
Rotator Cuff/pathology
Tomography
X-Ray Computed

Computed tomography
Deep learning
Muscle atrophy
Rotator cuff
Sarcopenia
medicine
Radiology
Nuclear Medicine and imaging

mri
Neuroradiology
supraspinatus
business.industry
Ultrasound
deep learning
computed tomography
030229 sport sciences
General Medicine
medicine.disease
620 Engineering
Arthroplasty
rotator cuff
Muscular Atrophy
fatty infiltration
medicine.anatomical_structure
Adipose Tissue
Musculoskeletal
quantitative assessment
repair
570 Life sciences
biology
Radiology
Tomography
medicine.symptom
business
Zdroj: European radiology, vol. 31, no. 1, pp. 181-190
European Radiology
Taghizadeh, Elham; Truffer, Oskar; Becce, Fabio; Eminian, Sylvain; Gidoin, Stacey; Terrier, Alexandre; Farron, Alain; Büchler, Philippe (2021). Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets. European radiology, 31(1), pp. 181-190. Springer-Verlag 10.1007/s00330-020-07070-7
DOI: 10.1007/s00330-020-07070-7
Popis: Objectives This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. Methods One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Results Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p R2 = 0.87), fatty infiltration (R2 = 0.91), and overall muscle degeneration (R2 = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R2 = 0.61) than human raters (R2 = 0.87). Conclusions Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. Key Points • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters.
Databáze: OpenAIRE