A semi-automated method for liver tumor segmentation based on 2D region growing with

Source: OAI

ABSTRACT Liver tumour segmentation from computed tomography (CT) scans is a challenging task. A semi-automatic method based on 2D region growing with knowledge-based constraints is proposed to segment lesions from constituent 2D slices obtained from 3D CT images. Minimal user involvement is required to define an approximate region of interest around the suspected legion area. The seed point and feature vectors are then calculated and voxels are labeled using a region-growing approach. Knowledge-based constraints are incorporated into the method to ensure the size and shape of the segmented region is within acceptable parameters. The individual segmented lesions can then be stacked together to generate a 3D volume. The proposed method was tested on a training set of 10 tumours and a testing set of 10 tumours. To evaluate the results quantitatively, various measures were used to generate scores. Based on the results obtained from the 10 testing tumours, the method was resulted in an average score of 64. This work is supported by a research grant (SBIC RP C-008/2006) from the Singapore BioImaging Consortium, Agency for Science, Technology and Research.

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    ABSTRACT: Automatic lesion detection is important for cancer examination and treatment, whereas it remains challenging due to the varied shape, size, and contextual anatomy of the diseased masses. In this paper, we present a robust and effective learning based method for automatic detection of liver lesions from computed tomography data. The contributions of this paper are the following. First, we develop a cascade learning approach to lesion detection comprising multiple detectors in the spirit of marginal space learning. Second, a gradient based locally adaptive segmentation method is proposed for solid liver lesions. The segmentation results are used to extract informative features for classification of generated candidates. Extensive experimental validation is carried out on 660 volumes with 1,302 hypodense lesions, and 234 volumes with 328 hyperdense lesions, with a resulting 90% detection rate at 1.01 false positives per volume for hypodense lesion and 1.58 false positives per volume for hyperdense lesion, respectively.
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on; 01/2012
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    Ecole des Mines d'Alès, 01/2008, Degree: PhD
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    ABSTRACT: In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.
    IEEE transactions on medical imaging. 04/2012; 31(9):1698-712.

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