Department of Internal Medicine I, Ulm University, Ulm, Germany.
Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstraße 60, 70174, Stuttgart, Germany.
Sci Rep. 2017 Oct 6;7(1):12779. doi: 10.1038/s41598-017-12925-z.
Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation.
对患有胰腺癌的患者进行超声图像的肝转移手动分割是常见的做法。半自动测量方法有望在该过程中提供帮助，通常使用已经了解算法的检查者对少数病变进行评估。在这项工作中，我们提出了一种使用一组 105 个不同转移灶图像的算法来分割胰腺癌肝转移的应用。该算法和两位检查者在评估图像之前从未使用过该算法。检查者首先进行手动分割，然后在五周后使用该算法进行半自动分割。他们对半自动分割结果的满意度高达 90%。使用该算法的速度明显更快，其平均骰子相似系数超过 80%。使用组内相关系数对操作者间变异性的估计良好，为 0.8。总之，该算法可快速准确地分割肝转移，与手动分割这一金标准相当。
