DATASET for "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment"

Here we provide the entire dataset and code for the article "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment" published in Computers in Biology and Medicine (DOI: 10.1016/j.compbiomed.2021.104623). Any results obtained using the published dataset MUST cite the original research article (https://www.sciencedirect.com/science/article/abs/pii/S0010482521004170). See the ReadMe file "readme.txt" for all details. Abstract: "Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 years ± 21, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images. Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson’s correlation coefficient (p<0.001, test set). The ICC(A,1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community."

本文提供发表于《Computers in Biology and Medicine》（DOI: 10.1016/j.compbiomed.2021.104623）的论文《用于神经肌肉疾病评估的横向肌肉骨骼超声图像深度学习分割》的完整数据集与代码。使用该公开数据集所得的任何研究成果，均需引用该原创研究论文（https://www.sciencedirect.com/science/article/abs/pii/S0010482521004170）。详细信息请参阅自述文件“readme.txt”。

摘要：超声成像是一种便于患者接受且稳定性优异的技术，可用于研究生理与病理状态下的肌肉组织。用于超声图像分析的自动化深度学习（Deep Learning, DL）系统可辅助专业操作人员，能够在减少人工干预的前提下对大规模数据集开展研究，具备重要应用价值。本研究旨在提出一种针对横向肌肉骨骼超声图像的横截面面积（Cross-Sectional Area, CSA）分割深度学习算法，同时提供可用于肌肉研究的定量灰度分析方法，并在大规模数据集上验证该算法的性能。本次数据集共包含1283名受试者的3917张图像，涉及肱二头肌、胫骨前肌及腓肠肌内侧头，受试者平均年龄为50±21岁，其中男性729名。该算法基于多种深度学习架构构建，并将其分割性能与专家手动分割结果进行了对比。我们采用Bland-Altman图（Bland-Altman plots）与相关性分析，对比了自动分割与手动分割的横截面面积内的平均灰度值。我们采用灰度值Z分数（Z-scores），对比了自动分割与手动分割在健康肌肉与异常肌肉分类任务中的表现。在测试集上，该算法的精确率（Precision）为0.88±0.12，召回率（Recall）为0.92±0.09。算法在异常肌肉图像上的分割性能略有下降，但仍未损失对健康与异常肌肉图像的区分能力。Bland-Altman图显示误差分布无明显趋势，两次测量的Pearson相关系数（Pearson’s correlation coefficient）为0.99（p<0.001，测试集）。两次Z分数测量值的组内相关系数ICC(A,1)（Intraclass Correlation Coefficient ICC(A,1)）为0.99。该算法可实现稳定性优异的CSA分割，并可输出与手动操作相当的平均灰度水平信息。该算法可为临床医师开展神经肌肉疾病的诊断与随访提供有力辅助工具。本研究将完整数据集与代码公开，以供科研社区使用。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于神经肌肉疾病评估的医学图像数据集，包含3917张横断面肌肉骨骼超声图像，覆盖肱二头肌、胫骨前肌和内侧腓肠肌，来自1283名受试者。它支持深度学习算法进行横截面积分割和灰度分析，旨在辅助临床诊断，并提供代码和完整数据供研究社区使用。