利用基于CT参考肺形状训练的主动形状模型对功能性SPECT图像进行自动肺分割。
Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.
作者信息
Cheimariotis Grigorios-Aris, Al-Mashat Mariam, Haris Kostas, Aletras Anthony H, Jögi Jonas, Bajc Marika, Maglaveras Nicolaos, Heiberg Einar
机构信息
Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Department of Clinical Sciences Lund, Clinical Physiology, Skåne University Hospital, Lund University, Lund, Sweden.
出版信息
Ann Nucl Med. 2018 Feb;32(2):94-104. doi: 10.1007/s12149-017-1223-y. Epub 2017 Dec 13.
OBJECTIVE
Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes.
METHODS
A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images.
RESULTS
The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was - 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004).
CONCLUSION
Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.
目的
图像分割是量化肺功能降低或丧失程度的关键步骤。本研究旨在开发并验证一种用于在通气和灌注单光子发射计算机断层扫描（SPECT）图像中自动分割肺部的新工具，并将自动和手动SPECT肺部分割与参考计算机断层扫描（CT）体积进行比较。
方法
共有77名受试者（69例阻塞性肺疾病患者和8例无明显通气或灌注丧失的受试者）在混合伽马相机系统中先进行低剂量CT检查，随后进行通气/灌注（V/P）SPECT检查。在训练阶段，利用57张解剖学低剂量CT图像中的肺形状构建两个主动形状模型（右肺和左肺），然后将其用于图像分割。该算法在20例患者中进行了验证，将其结果与相应CT图像的参考轮廓进行比较，并将SPECT图像中的自动分割与手动轮廓进行比较。
结果
自动SPECT轮廓与手动SPECT轮廓之间的骰子系数，右肺为0.83±0.04%，左肺为0.82±0.05%。在SPECT中，右肺（R = 0.53，p = 0.02）和左肺（R = 0.69，p < 0.001）的CT参考体积与自动轮廓之间存在统计学显著差异。在比较SPECT图像中CT参考体积与手动轮廓时，左肺（偏差为-10±491，R = 0.60，p = 0.005）和右肺（偏差为36±524 ml，R = 0.62，p = 0.004）也有类似的观察结果。
结论
SPECT图像上的自动分割与SPECT图像上的手动分割相当。功能性SPECT图像的手动轮廓与解剖学CT图像之间存在相对较大的体积差异，这证实了功能性SPECT图像的肺分割是一项具有挑战性的任务。当前算法是朝着自动量化广泛测量迈出的第一步。
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