Paper Info
Title
Heart Motion Tracking On Cine Mri Based On A Deep Boltzmann Machine-Driven Level Set Method
Abstract
Tracking the heart motion during radiation treatment of cancer patients can provide important information for designing strategies to reduce radiation-induced heart toxicity. Recently, in-treatment cine MRI images are used for guiding radiation therapy. However, dynamic changes of heart shape and limited-contrast of cine MRI images make automatic heart motion tracking a very challenging task. This paper proposes a deep generative shape model-driven level set method to address these challenges and automatically track heart motion on 2D cine MRI images. First, we use a three-layered Deep Boltzmann Machine (DBM) to train a heart shape model that can characterize both global and local heart shape variations. Second, the shape priors inferred from the trained heart shape model are incorporated into the distance regularized level set evolution-based segmentation method to guide frame-by-frame heart segmentation on cine MRI images. We demonstrate the superior performance of the proposed method on cine MRI image sequences acquired from seven volunteers and also compare it with four other methods.
Year
Venue
Keywords
2018
2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018)
Heart motion tracking, generative shape model, deep Boltzmann machine, DRLSE (Distance Regularized Level Set Evolution)
Field
DocType
ISSN
Heart shape,Computer vision,Boltzmann machine,Pattern recognition,Level set method,Computer science,Segmentation,Level set,Artificial intelligence,Heart motion
Conference
1945-7928
Citations 
PageRank 
References 
0
0.34
0
Authors
13
Name
Order
Citations
PageRank
Jian Wu111.05
Ruan Su255953.00
Thomas Mazur311.05
Nalini Daniel410.71
Hilary Lashmett510.71
Laura Ochoa610.71
Imran Zoberi710.71
Chunfeng Lian813222.61
h michael gach911.73
Sasa Mutic1011.39
Maria Thomas1110.71
Mark A. Anastasio1210525.53
Hua Li13459.03