MULTI-SCALE SEGMENTATION USING DEEP GRAPH CUTS: ROBUST LUNG TUMOR DELINEATION IN MVCBCT

被引：0
|
作者：
Wu, Xiaodong [1 ,2 ]
Zhong, Zisha [1 ,2 ]
Buatti, John [2 ]
Bai, Junjie [1 ]
机构：
[1] Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA
[2] Univ Iowa, Dept Radiat Oncol, Iowa City, IA 52242 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：
Deep graph cuts; multi-scale image segmentation; deep networks; lung tumor segmentation; CONVOLUTIONAL NEURAL-NETWORKS;
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：
Deep networks have been used in a growing trend in medical image analysis with the remarkable progress in deep learning. In this paper, we formulate the multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. The performance of the proposed method is assessed on the application of lung tumor segmentation in 38 mega-voltage cone-beam computed tomography datasets.
引用
收藏
页码：514 / 518
页数：5
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