论文标题
分析脊柱X射线图像的脊柱侧弯
Analysis of Scoliosis From Spinal X-Ray Images
论文作者
论文摘要
脊柱侧弯是一种先天性疾病，其中脊柱从正常形状中变形。脊柱侧弯的测量需要在脊柱中标记和鉴定椎骨。脊柱X光片是对脊柱进行成像的最具成本效率和可访问的方式。脊柱X光片中可靠，准确的椎骨分割对于图像引导的脊柱评估，疾病诊断和治疗计划至关重要。常规评估依赖于乏味且耗时的手动测量，这可能会受到观察者间的可变性。文献中无法准确识别和分割相关的椎骨的全自动方法。利用经过逐步调整的U-NET模型，我们提出了一个端到端分割模型，该模型提供了与脊柱侧弯测量相关的椎骨的全自动且可靠的分割。我们来自一组前后脊柱X射线图像的实验结果表明，我们的模型的平均骰子得分为0.993，有望成为脊柱椎骨识别和标记的有效工具，最终帮助医生在可靠的脊柱侧弯估计中进行了可靠的估计。此外，对分段椎骨的COBB角度的估计进一步证明了我们模型的有效性。
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spine radiographs are the most cost-effective and accessible modality for imaging the spine. Reliable and accurate vertebrae segmentation in spine radiographs is crucial in image-guided spinal assessment, disease diagnosis, and treatment planning. Conventional assessments rely on tedious and time-consuming manual measurement, which is subject to inter-observer variability. A fully automatic method that can accurately identify and segment the associated vertebrae is unavailable in the literature. Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement. Our experimental results from a set of anterior-posterior spine X-Ray images indicate that our model, which achieves an average Dice score of 0.993, promises to be an effective tool in the identification and labeling of spinal vertebrae, eventually helping doctors in the reliable estimation of scoliosis. Moreover, estimation of Cobb angles from the segmented vertebrae further demonstrates the effectiveness of our model.
