Hermoye Laurent, Laamari-Azjal Ismael, Cao Zhujiang, Annet Laurence, Lerut Jan, Dawant Benoit M, Van Beers Bernard E
Diagnostic Radiology Unit and Center for Anatomical, Functional and Molecular Imaging Research, Université Catholique de Louvain, Saint-Luc University Hospital, Avenue Hippocrate 10, B-1200 Brussels, Belgium.
Radiology. 2005 Jan;234(1):171-8. doi: 10.1148/radiol.2341031801. Epub 2004 Nov 24.
To compare the accuracy and repeatability of a semiautomatic segmentation algorithm with those of manual segmentation for determining liver volume in living liver transplant donors at magnetic resonance (MR) imaging.
The institutional review board approved this retrospective study and waived the requirement for informed consent. The semiautomatic segmentation algorithm is based on geometric deformable models and the level-set technique. It entails (a) placing initialization circle(s) on each image section, (b) running the algorithm, (c) inspecting and possibly manually modifying the contours obtained with the segmentation algorithm, and (d) placing lines to separate the liver segments. For 18 living donors (eight men and 10 women; mean age, 34 years; age range, 25-46 years), two observers each performed two semiautomatic and two manual segmentations on contrast material-enhanced T1-weighted MR images. Each measurement was timed. Actual graft weight was measured during surgery. The time needed for manual and that needed for semiautomatic segmentation were compared. Accuracy and repeatability were evaluated with the Bland-Altman method.
Mean interaction time was reduced from 25 minutes with manual segmentation to 5 minutes with semiautomatic segmentation. The mean total time for the semiautomatic process was 7 minutes 20 seconds. Differences between the actual volume and the estimated volume ranged from -223 to +123 mL for manual segmentation and from -214 to +86 mL for semiautomatic segmentation. The 95% limits of agreement for the ratio of actual graft volume to estimated graft volume were 0.686 and 1.601 for semiautomatic segmentation and 0.651 and 1.957 for manual segmentation. Semiautomatic segmentation improved estimation in 15 of 18 cases. Inter- and intraobserver repeatability was higher with semiautomatic segmentation.
Use of the semiautomatic segmentation algorithm substantially reduces the time needed for volumetric measurement of liver segments while improving both accuracy and repeatability.
比较半自动分割算法与手动分割算法在磁共振（MR）成像中确定活体肝移植供体肝脏体积时的准确性和可重复性。
机构审查委员会批准了这项回顾性研究，并免除了知情同意的要求。半自动分割算法基于几何可变形模型和水平集技术。它包括（a）在每个图像切片上放置初始化圆，（b）运行算法，（c）检查并可能手动修改通过分割算法获得的轮廓，以及（d）放置线条以分隔肝段。对于18名活体供体（8名男性和10名女性；平均年龄34岁；年龄范围25 - 46岁），两名观察者分别在对比剂增强的T1加权MR图像上进行了两次半自动分割和两次手动分割。每次测量都记录了时间。手术期间测量了实际移植肝重量。比较了手动分割和半自动分割所需的时间。使用Bland-Altman方法评估准确性和可重复性。
平均交互时间从手动分割的25分钟减少到半自动分割的5分钟。半自动过程的平均总时间为7分20秒。手动分割时实际体积与估计体积的差异范围为 - 223至 + 123 mL，半自动分割时为 - 214至 + 86 mL。实际移植肝体积与估计移植肝体积之比的95%一致性界限，半自动分割为0.686和1.601，手动分割为0.651和1.957。半自动分割在18例中的15例中改善了估计。半自动分割的观察者间和观察者内可重复性更高。
使用半自动分割算法可大幅减少肝段体积测量所需的时间，同时提高准确性和可重复性。
