Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model.
Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model.
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DOI：
10.3390/e25040662
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发表时间：
2023-04-14
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肌肉骨骼超声成像是早期筛查和准确治疗肌肉疾病的重要依据。它允许观察肌肉状态以筛查潜在的神经肌肉疾病，包括重症肌无力、强直性肌营养不良和强直性肌营养不良。由于骨骼肌超声图像噪声的复杂性，对其进行分析是一个繁琐而耗时的过程。因此，我们提出了一种基于多任务学习的方法来自动分割和初步诊断横向肌肉骨骼超声图像。该方法通过构建基于多尺度融合和注意机制的多任务模型（MMA-Net）实现肌肉横截面积（CSA）分割和异常肌肉分类。该模型利用任务之间的相关性，通过共享一部分浅网络和添加连接来交换深网络中的信息。在MMA-Net中加入多尺度特征融合模块和注意机制，以增加感受野，增强特征提取能力。使用来自多个受试者的总共1827个内侧腓肠肌超声图像进行实验。随机选择10%的样本进行测试，10%作为验证集，其余80%作为训练集。结果表明，提出的网络结构和增加的模块是有效的。与先进的单任务模型和现有的分析方法相比，我们的方法具有更好的分类和分割性能。肌肉横截面积分割的平均Dice系数和IoU分别为96.74%和94.10%。异常肌肉分类的准确率和召回率分别为95.60%和94.96%。该方法实现了对横向肌肉骨骼超声图像方便、准确的分析，可以从多个角度辅助医生诊断和治疗肌肉疾病。
Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.
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影响因子：
--
作者：
Wang B
通讯作者：
Wang B
影响因子：
4.9
作者：
METZ, CE
通讯作者：
METZ, CE
影响因子：
2.9
作者：
Caresio, Cristina;Salvi, Massimo;Minetto, Marco Alessandro
通讯作者：
Minetto, Marco Alessandro
影响因子：
2.9
作者：
Salvi, Massimo;Caresio, Cristina;Minetto, Marco Alessandro
通讯作者：
Minetto, Marco Alessandro
影响因子：
2.9
作者：
Arts, Ilse M. P.;van Rooij, Frank G.;Zwarts, Machiel J.
通讯作者：
Zwarts, Machiel J.