Brown M S, McNitt-Gray M F, Goldin J G, Greaser L E, Hayward U M, Sayre J W, Arid M K, Aberle D R
Department of Radiological Sciences, UCLA School of Medicine, Los Angeles, CA 90095-1721, USA.
J Comput Assist Tomogr. 1999 Jul-Aug;23(4):632-40. doi: 10.1097/00004728-199907000-00027.
The goal of this work was to develop an automated method for calculating single (SLV) and total (TLV) lung volumes from CT images.
Patients underwent volumetric CT scanning through the entire chest in a single breath-hold, as well as pulmonary function tests. An automated, knowledge-based system was developed to segment the lungs in the CT images. Image-processing routines were used to extract sets of voxels from the image data that were identified by matching them to anatomical objects defined in a model. SLV and TLV were calculated by summing included voxels.
For 43 patients analyzed, TLV from CT and total lung capacity from body plethysmography were strongly correlated (r = 0.90). On average, the CT-derived volume of the left lung accounted for 47.2% of the total.
A knowledge-based approach to segmentation of the lungs in CT can be used to automatically estimate SLV and TLV.
本研究的目的是开发一种从CT图像计算单肺容积（SLV）和总肺容积（TLV）的自动化方法。
患者在一次屏气过程中对整个胸部进行容积CT扫描，并进行肺功能测试。开发了一种基于知识的自动化系统，用于在CT图像中分割肺。图像处理程序用于从图像数据中提取体素集，这些体素通过将其与模型中定义的解剖对象进行匹配来识别。通过对包含的体素求和来计算SLV和TLV。
对43例患者进行分析，CT得出的TLV与体容积描记法得出的肺总量密切相关（r = 0.90）。平均而言，CT得出的左肺容积占总量的47.2%。
基于知识的CT肺分割方法可用于自动估计SLV和TLV。
