Kjønigsen Lisa Jannicke, Harneshaug Magnus, Fløtten Ann-Monica, Karterud Lena Korsmo, Petterson Kent, Skjolde Grethe, Eggesbø Heidi B, Weedon-Fekjær Harald, Henriksen Hege Berg, Lauritzen Peter M
Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
The Centre for Old Age Psychiatry Research, Innlandet Hospital Trust, Ottestad, Norway.
Eur Radiol Exp. 2019 Oct 30;3(1):42. doi: 10.1186/s41747-019-0122-5.
Segmentation of computed tomography (CT) images provides quantitative data on body tissue composition, which may greatly impact the development and progression of diseases such as type 2 diabetes mellitus and cancer. We aimed to evaluate the inter- and intraobserver variation of semiautomated segmentation, to assess whether multiple observers may interchangeably perform this task.
Anonymised, unenhanced, single mid-abdominal CT images were acquired from 132 subjects from two previous studies. Semiautomated segmentation was performed using a proprietary software package. Abdominal muscle compartment (AMC), inter- and intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were identified according to pre-established attenuation ranges. The segmentation was performed by four observers: an oncology resident with extensive training and three radiographers with a 2-week training programme. To assess interobserver variation, segmentation of each CT image was performed individually by two or more observers. To assess intraobserver variation, three of the observers did repeated segmentations of the images. The distribution of variation between subjects, observers and random noise was estimated by a mixed effects model. Inter- and intraobserver correlation was assessed by intraclass correlation coefficient (ICC).
For all four tissue compartments, the observer variations were far lower than random noise by factors ranging from 1.6 to 3.6 and those between subjects by factors ranging from 7.3 to 186.1. All interobserver ICC was ≥ 0.938, and all intraobserver ICC was ≥ 0.996.
Body composition segmentation showed a very low level of operator dependability. Multiple observers may interchangeably perform this task with highly reproducible results.
计算机断层扫描（CT）图像的分割可提供有关身体组织成分的定量数据，这可能对2型糖尿病和癌症等疾病的发生和发展产生重大影响。我们旨在评估半自动分割的观察者间和观察者内差异，以评估多个观察者是否可以互换执行此任务。
从之前两项研究的132名受试者中获取匿名的、未增强的腹部中部单张CT图像。使用专有软件包进行半自动分割。根据预先确定的衰减范围识别腹部肌肉隔室（AMC）、肌内和肌间脂肪组织（IMAT）、内脏脂肪组织（VAT）和皮下脂肪组织（SAT）。分割由四名观察者进行：一名经过广泛培训的肿瘤学住院医师和三名接受了为期两周培训计划的放射技师。为了评估观察者间差异，由两名或更多观察者分别对每张CT图像进行分割。为了评估观察者内差异，三名观察者对图像进行重复分割。通过混合效应模型估计受试者、观察者和随机噪声之间的差异分布。通过组内相关系数（ICC）评估观察者间和观察者内的相关性。
对于所有四个组织隔室，观察者间差异比随机噪声低得多，倍数范围为1.6至3.6，受试者之间的差异倍数范围为7.3至186.1。所有观察者间ICC均≥0.938，所有观察者内ICC均≥0.996。
身体成分分割显示出极低的操作者依赖性。多个观察者可以互换执行此任务，结果具有高度可重复性。
