深度学习用于镰状细胞病患者脾脏长度的自动测量
Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients
摘要：
镰状细胞病（SCD）是世界上最常见的遗传性疾病之一。脾肿大（脾脏异常肿大）在SCD儿童中很常见。如果不及时治疗，脾肿大可能危及生命。目前测量脾脏大小的工作流程包括触诊，可能随后是2D超声成像中的手动长度测量。然而，这种手动测量依赖于操作员的专业知识，并受到观察者内和观察者间的变化。我们研究了使用深度学习来从超声图像中自动估计脾脏长度。我们研究了两种类型的方法，一种是基于分割的，一种是基于直接长度估计的，并将结果与人类专家的测量结果进行比较。我们的最佳模型（基于分割）实现了7.42%的百分比长度误差，接近观察者间变异性水平（5.47%-6.34%）。据我们所知，这是首次尝试从超声图像中以全自动方式测量脾脏大小。
英文摘要：
Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%-6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.
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