IMPROVING TUMOR CO-SEGMENTATION ON PET-CT IMAGES WITH 3D CO-MATTING

被引：0
|
作者：
Zhong, Zisha [1 ]
Kim, Yusung [2 ]
Zhou, Leixin [1 ]
Plichta, Kristin [2 ]
Allen, Bryan [2 ]
Buatti, John [2 ]
Wu, Xiaodong [1 ,2 ]
机构：
[1] Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA
[2] Univ Iowa, Dept Radiat Oncol, Iowa City, IA USA
来源：
2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | 2018年
基金：
美国国家卫生研究院; 美国国家科学基金会;
关键词：
image segmentation; interactive segmentation; lung tumor segmentation; image matting; co-segmentation;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Positron emission tomography and computed tomography (PET-CT) plays a critically important role in modern cancer therapy. In this paper, we focus on automated tumor delineation on PET-CT image pairs. Inspired by co-segmentation model, we develop a novel 3D image co-matting technique making use of the inner-modality information of PET and CT for matting. The obtained co-matting results are then incorporated in the graph-cut based PET-CT co-segmentation framework. Our comparative experiments on 32 PET-CT scan pairs of lung cancer patients demonstrate that the proposed 3D image co-matting technique can significantly improve the quality of cost images for the co-segmentation, resulting in highly accurate tumor segmentation on both PET and CT scan pairs.
引用
收藏
页码：224 / 227
页数：4
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