A novel @entity96 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 @entity5 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 @entity1 data, and artificially generated samples. The @entity1 data included preoperative RFA images and a public data set from "3D @entity96 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 @entity5 with very low contrast and ambiguous borders, and the performance remained high with noisy image data.

XXXX segmentation with hidden Markov measure field model and non-parametric distribution estimation.

@entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'tumors'] @entity1 :: ('9606', 'Species') :: ['patient'] @entity96 :: ('MESH:D008113', 'Disease') :: ['Liver Tumor', 'liver tumor']
@entity96 :: (MESH:D008113,Disease) :: ['Semi-automatic liver tumor']
