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Automatic Vertebral Column Extraction by Whole-Body Bone SPECT Scan

机译:全身骨骼SPECT扫描自动椎体柱提取

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Bone extraction and division can enhance the accuracy of diagnoses based on whole-body bone SPECT data. This study developed a method for using conventional SPECT for automatic recognition of the vertebral column. A novel feature of the proposed approach is a novel “bone graph" image description method that represents the connectivity between these image regions to facilitate manipulation of morphological relationships in the skeleton before surgery. By tracking the paths shown on the bone graph, skeletal structures can be identified by performing morphological operations. The performance of the method was evaluated quantitatively and qualitatively by two experienced nuclear medicine physicians. Datasets for whole-body bone SPECT scans in 46 lung cancer patients with bone metastasis were obtained with Tc-99m MDP. The algorithm successfully segmented vertebrae in the thoracolumbar spine. The quantitative assessment shows that the segmentation method achieved an average TP, FP, and FN rates of 95.1%, 9.1%, and 4.9%. The qualitative evaluation shows an average acceptance rate of 83%, where the data for the acceptable and unacceptable groups had a Cronbach’s alpha value of 0.718, which indicated reasonable internal consistency and reliability.
机译:骨提取和划分可以基于全身骨质SPECT数据提高诊断的准确性。该研究开发了一种使用传统SPECT进行自动识别椎体柱的方法。所提出的方法的新颖特征是一种新颖的“骨图”图像描述方法,其代表这些图像区域之间的连接,以便于在手术前促进操纵骨架中的形态关系。通过跟踪骨骼图上所示的路径,骨架结构可以通过进行形态操作来鉴定。通过两种经验丰富的核医生定量和定性评估该方法的性能。通过TC-99M MDP获得46例肺癌患者的全身骨质SPECT扫描的数据集。该算法在胸腰椎上成功分段椎骨。定量评估表明,分段方法实现了95.1%,9.1%和4.9%的平均TP,FP和FN率。定性评估显示83%的平均接受率,在哪里可接受和不可接受的群体的数据具有0.718的Cronbach的alpha值,其中指示Rea可安静的内部一致性和可靠性。

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