Molecular Biological Approaches in Atherosclerosis Research

2015 ◽  
pp. 79-82
Author(s):  
Horst Kather
Keyword(s):  
Atherosclerosis Research

scholarly journals Transgenic rabbits as models for atherosclerosis research

1999 ◽  
Vol 40 (3) ◽  
pp. 365-375 ◽  
Author(s):  
Margaret E. Brousseau ◽  
Jeffrey M. Hoeg
Keyword(s):  
Transgenic Rabbits ◽  
Atherosclerosis Research

scholarly journals Effect of variation in the apo A-IV gene on body mass index and fasting and postprandial lipids in the European Atherosclerosis Research Study II

1999 ◽  
Vol 40 (2) ◽  
pp. 287-294 ◽  
Author(s):  
Rachel M. Fisher ◽  
Heidi Burke ◽  
Viviane Nicaud ◽  
Christian Ehnholm ◽  
Steve E. Humphries
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Research Study ◽  
Postprandial Lipids ◽  
Atherosclerosis Research

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Image

2021 ◽  
Author(s):  
J.M. Murray ◽  
P. Pfeffer ◽  
R. Seifert ◽  
A. Hermann ◽  
J. Handke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Atherosclerotic Plaque ◽  
State Of The Art ◽  
The Mean ◽  
Time Savings ◽  
Plaque Quantification ◽  
Atherosclerosis Research ◽  
Hematoxylin Eosin ◽  
Microscopy Images

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91±0.15 for plaque and 0.97±0.08 for lumen pixels. The mean accuracy is 0.98±0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.

The Development and Use of Animal Models in Atherosclerosis Research

1987 ◽  
pp. 337-357 ◽  
Author(s):  
Robert W. Wissler ◽  
Dragoslava Vesselinovitch
Keyword(s):  
Animal Models ◽  
Atherosclerosis Research
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