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

页码：514 / 518

页数：5

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