Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning
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Jae Chul Yoo | Baek Hwan Cho | Sunhyun Yook | In Young Kim | Kyeongwon Cho | Joo Young Kim | Kyunghan Ro | Sungmin You | Bo Rum Nam | Hee Seol Park | Eunkyoung Park | B. Cho | Eunkyoung Park | Sunhyun Yook | In Young Kim | Kyeongwon Cho | J. Yoo | Joo Young Kim | Kyunghan Ro | Sungmin You | B. Nam | H. Park | J. Kim | I. Kim
[1] George E. Dahl,et al. Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. , 2018, Archives of pathology & laboratory medicine.
[2] Y. Rhee,et al. Factors Predictive of Healing in Large Rotator Cuff Tears: Is It Possible to Predict Retear Preoperatively? , 2018, The American journal of sports medicine.
[3] Daniel S. Kermany,et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.
[4] Sang Min Lee,et al. Supraspinatus muscle occupation ratio predicts rotator cuff reparability. , 2017, Journal of shoulder and elbow surgery.
[5] J. S. Park,et al. Can Preoperative Magnetic Resonance Imaging Predict the Reparability of Massive Rotator Cuff Tears? , 2017, The American journal of sports medicine.
[6] Deukhee Lee,et al. Automatic segmentation of supraspinatus from MRI by internal shape fitting and autocorrection , 2017, Comput. Methods Programs Biomed..
[7] G. Litjens,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[8] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[9] Meng Yang,et al. Large-Margin Softmax Loss for Convolutional Neural Networks , 2016, ICML.
[10] Trevor Darrell,et al. Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] J. Oh,et al. Evaluation of Fatty Degeneration of the Supraspinatus Muscle Using a New Measuring Tool and Its Correlation Between Multidetector Computed Tomography and Magnetic Resonance Imaging , 2011, The American journal of sports medicine.
[13] S. Burkhart,et al. The geometric classification of rotator cuff tears: a system linking tear pattern to treatment and prognosis. , 2010, Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association.
[14] P. Brassard,et al. Atrophy and fatty infiltration of the supraspinatus muscle: sonography versus MRI. , 2008, AJR. American journal of roentgenology.
[15] N Morcet,et al. Prediction of Rotator Cuff Repair Results by Magnetic Resonance Imaging , 1997, Clinical orthopaedics and related research.
[16] Y. Rolland,et al. Atrophy of the supraspinatus belly. Assessment by MRI in 55 patients with rotator cuff pathology. , 1996, Acta orthopaedica Scandinavica.
[17] S. Tamai,et al. Function of supraspinatus muscle with torn cuff evaluated by magnetic resonance imaging. , 1995, Clinical orthopaedics and related research.
[18] D. Goutallier,et al. Fatty infiltration of disrupted rotator cuff muscles. , 1995, Revue du rhumatisme.
[19] D Goutallier,et al. Fatty muscle degeneration in cuff ruptures. Pre- and postoperative evaluation by CT scan. , 1994, Clinical orthopaedics and related research.
[20] Mclaughlin Hl. Lesions of the musculotendinous cuff of the shoulder. The exposure and treatment of tears with retraction. 1944. , 1994 .
[21] D. Patte,et al. Classification of rotator cuff lesions. , 1990, Clinical orthopaedics and related research.
[22] H Ellman,et al. Diagnosis and treatment of incomplete rotator cuff tears. , 1990, Clinical orthopaedics and related research.