用于腹侧体成分分析的CT图像的自动分割
Automatic segmentation of CT images for ventral body composition analysis
摘要：
目的：人体成分与许多疾病有关，包括糖尿病、癌症和心血管疾病。在本文中，我们开发了一个全自动的身体组织分解程序，分割三个主要的车厢，是与身体成分分析-皮下脂肪组织（SAT），内脏脂肪组织（VAT）和肌肉。在分割过程中还分割了三个额外的隔室-腹侧腔、肺和骨，以辅助主要隔室的分割。方法：建立一个具有密集连接层的卷积神经网络（CNN）模型来进行腹侧腔分割。开发了一种图像处理工作流程，以使用CNN模型分割任何患者CT中的腹侧腔，然后使用滞后阈值处理和随后的形态学操作将身体组织进一步分割成多个隔室。重要的是，首先分割腹侧腔，以允许准确分离腹侧腔内部和外部具有相似Hounsfield单位（HU）的隔室。结果：腹腔分割CNN模型在60个CT中用手动标记的腹腔进行训练和测试。腹侧腔分割的骰子评分（平均值+/-标准差）为0.966+/-0.012。在静脉（IV）和口服造影剂的CT数据集上进行测试，骨骼、VAT、SAT、肌肉和肺的Dice评分分别为0.96 +/-0.02，0.94 +/-0.06，0.96 +/-0.04，0.95 +/-0.04和0.99+/-0.01。非造影CT数据集的Dice评分分别为0.97+/-0.02，0.94 +/-0.07，0.93 +/-0.06，0.91 +/-0.04和0.99+/-0.01。结论：建立了一个自动分割腹侧体多个分区的体组织分解程序。所提出的方法能够从CT图像中完全自动量化3D腹侧体成分指标。
英文摘要：
Purpose: Body composition is known to be associated with many diseases including diabetes, cancers and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and muscle. Three additional compartments - the ventral cavity, lung and bones were also segmented during the segmentation process to assist segmentation of the major compartments. Methods: A convolutional neural network (CNN) model with densely connected layers was developed to perform ventral cavity segmentation. An image processing workflow was developed to segment the ventral cavity in any patient's CT using the CNN model, then further segment the body tissue into multiple compartments using hysteresis thresholding followed by morphological operations. It is important to segment ventral cavity firstly to allow accurate separation of compartments with similar Hounsfield unit (HU) inside and outside the ventral cavity. Results: The ventral cavity segmentation CNN model was trained and tested with manually labelled ventral cavities in 60 CTs. Dice scores (mean +/- standard deviation) for ventral cavity segmentation were 0.966+/-0.012. Tested on CT datasets with intravenous (IV) and oral contrast, the Dice scores were 0.96+/-0.02, 0.94+/-0.06, 0.96+/-0.04, 0.95+/-0.04 and 0.99+/-0.01 for bone, VAT, SAT, muscle and lung, respectively. The respective Dice scores were 0.97+/-0.02, 0.94+/-0.07, 0.93+/-0.06, 0.91+/-0.04 and 0.99+/-0.01 for non-contrast CT datasets. Conclusion: A body tissue decomposition procedure was developed to automatically segment multiple compartments of the ventral body. The proposed method enables fully automated quantification of 3D ventral body composition metrics from CT images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
