Title
A 3D Primary Vessel Reconstruction Framework with Serial Microscopy Images.
Abstract
Three dimensional microscopy images present significant potential to enhance biomedical studies. This paper presents an automated method for quantitative analysis of 3D primary vessel structures with histology whole slide images. With registered microscopy images of liver tissue, we identify primary vessels with an improved variational level set framework at each 2D slide. We propose a Vessel Directed Fitting Energy (VDFE) to provide prior information on vessel wall probability in an energy minimization paradigm. We find the optimal vessel cross-section associations along the image sequence with a two-stage procedure. Vessel mappings are first found between each pair of adjacent slides with a similarity function for four association cases. These bi-slide vessel components are further linked by Bayesian Maximum A Posteriori (MAP) estimation where the posterior probability is modeled as a Markov chain. The efficacy of the proposed method is demonstrated with 54 whole slide microscopy images of sequential sections from a human liver.
Year
DOI
Venue
2015
10.1007/978-3-319-24574-4_30
Lecture Notes in Computer Science
Field
DocType
Volume
Computer vision,Pattern recognition,Computer science,Markov chain,Level set,Posterior probability,Artificial intelligence,Maximum a posteriori estimation,Microscopy,Bayesian probability,Energy minimization,Bayes' theorem
Conference
9351
ISSN
Citations 
PageRank 
0302-9743
5
0.52
References 
Authors
0
7
Name
Order
Citations
PageRank
Yanhui Liang1166.50
Fusheng Wang2848.79
Darren Treanor312113.20
Derek R. Magee436335.94
George Teodoro515022.18
Yangyang Zhu671.24
Jun Kong710617.74