Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

Kim, Joo Young; Ro, Kyung Han; You, Sungmin; Nam, Bo Rum; Yook, Sunhyun; Park, Hee Seol; Yoo, Jae Chul; Park, Eun-Kyoung; Cho, Kyeongwon; Cho, Baek Hwan; Kim, In Young

doi:10.1016/j.cmpb.2019.105063

Cited by 35 publications

(28 citation statements)

References 15 publications

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“…We believe that each network of the parallel configuration could extract target and auxiliary features more effectively with our automated algorithm. It is worth exploring whether other network architectures, such as U-Net 21 , could perform segmentation, but our VGG-based models have already satisfied the criteria for clinical use 22 . Therefore, we did not evaluate other network architectures.…”

Section: Discussionmentioning

confidence: 99%

Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation

Cho

Park

et al. 2020

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Ménière's Disease (MD) is difficult to diagnose and evaluate objectively over the course of treatment. Recently, several studies have reported MD diagnoses by MRI-based endolymphatic hydrops (EH) analysis. However, this method is time-consuming and complicated. Therefore, a fast, objective, and accurate evaluation tool is necessary. The purpose of this study was to develop an algorithm that can accurately analyze EH on intravenous (IV) gadolinium (Gd)-enhanced inner-ear MRI using artificial intelligence (AI) with deep learning. In this study, we developed a convolutional neural network (CNN)based deep-learning model named INHEARIT (INner ear Hydrops Estimation via ARtificial InTelligence) for the automatic segmentation of the cochlea and vestibule, and calculation of the EH ratio in the segmented region. Measurement of the EH ratio was performed manually by a neuro-otologist and neuro-radiologist and by estimation with the INHEARIT model and were highly consistent (intraclass correlation coefficient = 0.971). This is the first study to demonstrate that automated EH ratio measurements are possible, which is important in the current clinical context where the usefulness of IV-Gd inner-ear MRI for MD diagnosis is increasing. Ménière's disease (MD) is a multifactorial disorder with typical symptoms of recurrent spontaneous attacks of vertigo, fluctuating hearing loss, tinnitus, and sensations of ear fullness. Endolymphatic hydrops (EH) is a pathological finding where the endolymphatic spaces are distended by enlargements of endolymphatic volume, a histologic hallmark of MD 1-3. According to a 1995 consensus statement from the Committee on Hearing and Equilibrium of the American Association of Otolaryngology-Head and Neck Surgery (AAO-HNS), "certain" MD cases can only be confirmed by the histological demonstration of EH in postmortem temporal bone specimens 4. Therefore in 2015, a committee of the Bárány Society revised the diagnostic criteria to remove the concept of "certain MD" 5. Thus far, the diagnostic criteria have been changed due to the lack of tools to objectively find EH during life. However, with the advancement of imaging technology, MRI can be used to identify endolymphatic hydrops in MD patients as an objective marker. In 2004, Duan et al. succeeded in visualizing EH in vivo for the first time in a guinea pig using 4.7 T MRI 6. Nakashima et al. succeeded in confirming EH after injecting contrast media through intratympanic (IT) and intravenous (IV) injections into MD patients using 3 T MRI 7,8. Recently, many reports have been published regarding the use of MRI to assess EH. In particular, IV gadolinium (Gd)-enhanced inner-ear MRI has shown good results 9,10. We have also proven through previous studies that IV-Gd inner-ear MRI is very useful for diagnosing MD by demonstrating the correlation of hydrops with

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Section: Discussionmentioning

confidence: 99%

Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation

Cho

Park

et al. 2020

Sci Rep

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“…Ten-fold cross-validation, which was used in a previous study, was performed to evaluate the performance of the developed algorithm 42 . Because the total number of images used for training in the network was 240 and the k value was set to 10, 24 images were used as the validation set and 216 images as the training set.…”

Section: Methodsmentioning

confidence: 99%

“…DSC was evaluated to compare the similarities using an index ranging between 0 (no segmentation overlap) and 1 (perfect segmentation overlap) 47 . Although the absolute value of DSC is difficult to interpret, some previous studies proposed that > 0.70 indicates excellent agreement between measurement pairs 42 , 48 . Accuracy, sensitivity, and specificity were used to evaluate the ability of the models to detect the regions.…”

Section: Methodsmentioning

confidence: 99%

Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI

Ro¹,

Kim

Park

et al. 2021

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Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = − 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.

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“…Kim et al trained a CNN model using a shoulder MRI dataset of 240 patients. The trained model identified the muscle region of the rotator cuff with an accuracy of 99.9% and graded fatty infiltration at a high level [40]. Taghizadeh et al also conducted a similar study using a shoulder computed tomography of 103 patients as a dataset.…”

Section: Deep Learning For Joint-specific Soft Tissue Diseasementioning

confidence: 99%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

Cited by 35 publications

References 15 publications

Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation

Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation

Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

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