Abstract 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.

+More

median dice similarity score semiautomatic segmentation the interoperator variability intra class correlation coefficient manual segmentation of hepatic metastases in ultrasound images

Alexander Hann,Lucas Bettac,Mark M. Haenle,Tilmann Graeter,Andreas W. Berger,Jens Dreyhaupt,Dieter Schmalstieg,Wolfram G. Zoller,Jan Egger,.Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound. 7 (1),.

Alexander Hann,Lucas Bettac,Mark M. Haenle,Tilmann Graeter,Andreas W. Berger,Jens Dreyhaupt,Dieter Schmalstieg,Wolfram G. Zoller,Jan Egger,"Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound" 7

Alexander Hann,Lucas Bettac,Mark M. Haenle,Tilmann Graeter,Andreas W. Berger,Jens Dreyhaupt,Dieter Schmalstieg,Wolfram G. Zoller,Jan Egger,"Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound"