Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.
Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.
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DOI：
10.1038/s41598-021-93026-w
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发表时间：
2021-07-23
影响因子：
4.6
通讯作者：
Yoo JC
中科院分区：
文献类型：
--
作者：
Ro K;Kim JY;Park H;Cho BH;Kim IY;Shim SB;Choi IY;Yoo JC
占位率和脂肪浸润是评价肩袖撕裂的重要指标。我们使用深度学习框架分析了占据率，并使用基于自动区域的Otsu阈值技术研究了冈上肌的脂肪浸润。利用基于区域的自动化Otsu阈值技术计算冈上肌脂肪浸润量。测量临床评估与深度神经网络相似度的分割病灶的平均Dice相似系数、准确性、敏感性、特异性和相对面积差，冈上肌的平均Dice相似系数、准确性、敏感性、特异性和相对面积差分别为0.97、99.84、96.89、99.92和0.07，冈上肌的平均Dice相似系数、准确性、敏感性、特异性和相对面积差分别为0.94、99.89、93.34、99.95和2.03。采用Otsu阈值法测量的脂肪浸润在Goutallier分级中差异显著（0级、0.06级、4.68级、20.10级、42.86级、4级、55.79,p < 0.0001）。Otsu阈值法显示，占领率与脂肪浸润呈中等负相关（ρ = - 0.75, p < 0.0001）。该研究纳入了240名随机选择的患者，这些患者于2015年1月至2016年12月接受了肩部磁共振成像（MRI）检查。我们使用全卷积深度学习算法，通过测量冈上肌的占比来定量检测窝和肌肉区域。采用Otsu阈值法对脂肪浸润进行客观评价。所提出的卷积神经网络能够快速准确地从肩部MRI中分割冈上肌和窝，从而自动计算占据比。采用改进的Otsu阈值法进行定量评价，可计算冈上肌脂肪浸润比例。我们期望通过量化肩部MRI的指标来提高诊断的效率和客观性。
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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影响因子：
3.7
作者：
BahadarKhan K;A Khaliq A;Shahid M
通讯作者：
Shahid M
影响因子：
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