Learning a Prior Model for Automatic Liver Lesion Segmentation in Follow-up CT Images

Language
en
Document Type
Report
Issue Date
2013-11-19
Issue Year
2013
Authors
Militzer, Arne
Tietjen, Christian
Hornegger, Joachim
Editor
Edition
1
Abstract

Liver tumors that are not surgically removed need to be closely monitored. A common procedure for their assessment involves acquiring CT images every few months and rating disease status based on the largest diameters of a subset of the lesions. The most prominent benefits of automatic lesion segmentation methods in this context are minimization of time consuming interaction and the possibility of volumetric measurements. While existing methods could be applied to each image individually, we propose to incorporate information gained from previous images of the same patient to enhance the segmentation. We learn a Probabilistic Boosting Tree that has an internal representation of tumor growth from a set of training images. Provided a baseline lesion segmentation, it can generate a patient specific lesion prior to guide the segmentation in a follow-up image. In this paper, we describe and compare different methods for building the growth model and integrating it into a segmentation system. The validity of the approach is shown in an experimental evaluation on a database of 14 patients. On the 17 pairs of baseline and follow-up images in this database, segmentation performance was measured once without and once with the proposed prior. When comparing the points of 90% sensitivity from each experiment, introducing the prior improved the precision of the segmentation from 82.7% to 91.9%. This corresponds to a reduction of the number of false positive voxels per true positive voxel by 57.8%.

Series
Technical reports / Department Informatik
Series Nr.
CS-2013-03
DOI
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