Department of Biomedical Engineering and Computational Science, Aalto University School of Science, P.O. Box 12200, FI-00076 Aalto, Finland.
Med Image Anal. 2012 Jan;16(1):140-9. doi: 10.1016/j.media.2011.06.006. Epub 2011 Jun 24.
A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.
提出了一种用于 CT 图像的肝脏肿瘤新分割方法。本研究的目的是通过提供准确和自动的肿瘤分割，可靠地减少射频消融（RFA）治疗计划中的人工劳动和时间。所开发的方法是半自动的，仅需要最小的用户交互。分割基于非参数化强度分布估计和隐马尔可夫测度场模型，并应用了球形先验。还提出了一种后处理操作，以去除对相邻组织的溢出。除了使用单个图像作为输入数据的常规方法外，还开发了一种使用多个对比相位图像的方法。该方法的准确性通过两组患者数据和人工生成的样本进行了验证。患者数据包括术前 RFA 图像和来自“2008 年 3D 肝脏肿瘤分割挑战赛”的公共数据集。该方法在 RFA 数据上取得了非常高的准确性，并且优于使用公共数据集评估的其他方法，平均重叠误差为 30.3%，比以前表现最好的半自动方法提高了 2.3 个百分点。平均体积差异为 23.5%，平均、均方根和最大表面距离误差分别为 1.87、2.43 和 8.09 毫米。即使对于对比度非常低且边界模糊的肿瘤，该方法也能产生良好的结果，并且即使在图像数据存在噪声的情况下，性能仍然很高。
