Fu Yabo, Ippolito Joseph E, Ludwig Daniel R, Nizamuddin Rehan, Li Harold H, Yang Deshan
Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA.
Med Phys. 2020 Nov;47(11):5723-5730. doi: 10.1002/mp.14465. Epub 2020 Oct 3.
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.
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 computed tomography (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.
The ventral cavity segmentation CNN model was trained and tested with manually labeled 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.
A body tissue decomposition procedure was developed to automatically segment multiple compartments of the ventral body. The proposed method enables fully automated quantification of three-dimensional (3D) ventral body composition metrics from CT images.
已知身体成分与包括糖尿病、癌症和心血管疾病在内的多种疾病相关。在本文中，我们开发了一种全自动的身体组织分解程序，以分割与身体成分分析相关的三个主要部分——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另外三个部分——腹腔、肺和骨骼，以辅助主要部分的分割。
开发了一种具有密集连接层的卷积神经网络（CNN）模型来进行腹腔分割。开发了一种图像处理工作流程，使用CNN模型在任何患者的计算机断层扫描（CT）中分割腹腔，然后使用滞后阈值分割，随后进行形态学操作，将身体组织进一步分割成多个部分。首先分割腹腔很重要，以便准确分离腹腔内外具有相似亨氏单位（HU）的部分。
使用60例CT中手动标记的腹腔对腹腔分割CNN模型进行了训练和测试。腹腔分割的骰子系数（均值±标准差）为0.966±0.012。在使用静脉内（IV）和口服对比剂的CT数据集上进行测试，骨骼、VAT、SAT、肌肉和肺的骰子系数分别为0.96±0.02、0.94±0.06、0.96±0.04、0.95±0.04和0.99±0.01。对于非对比CT数据集，相应的骰子系数分别为0.97±0.02、0.94±0.07、0.93±0.06、0.91±0.04和0.99±0.01。
开发了一种身体组织分解程序，以自动分割腹部的多个部分。所提出的方法能够从CT图像中对三维（3D）腹部身体成分指标进行全自动量化。
