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Lung tumor segmentation in PET images using graph cuts.

Abstract：

:The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Ballangan C,Wang X,Fulham M,Eberl S,Feng DD

doi

10.1016/j.cmpb.2012.10.009

subject

Has Abstract

pub_date

2013-03-01 00:00:00

pages

260-8

issue

3

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(12)00251-9

journal_volume

109

pub_type

杂志文章

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