Integration of a knowledge-based constraint into generative models with applications in semi-automatic segmentation of liver tumors

Accurate delineation of liver tumors in medical images is a vital step in diagnosis, treatment planning, and monitoring. In this paper, we utilize a generative model for segmentation of abnormal liver regions. After preprocessing of an input image, the ROI of the tumor is determined, and the boundary of the abnormal region in a single slice is specified. Then, the remaining slices are processed by a generative model that is enhanced by the integration of a constraint. We search for the boundary of the tumor by a probabilistic approach and obtain the solution using the Bayesian inference. The Kullback-Leibler divergence is used to measure the consistency of the results to the model's constraint. We evaluated the proposed method using synthetic and clinical data. In the public dataset, we achieved a Dice measure of 0.84 +/- 0.06, which outperforms state-of-the-art hepatic tumor segmentation algorithms. Concerning all available clinical images, the Dice index of the proposed method is 0.90 +/- 0.03. (C) 2019 Elsevier Ltd. All rights reserved.