Fully automatic ventricle detection from cardiac MR images using machine learning

1994 ◽  
Author(s):  
John J. Weng ◽  
Ajit Singh ◽  
Ming-Yee Chiu
Keyword(s):  
Machine Learning ◽  
Mr Images ◽  
Cardiac Mr ◽  
Fully Automatic

Fully Automatic Cardiac Segmentation And Quantification For Pulmonary Hypertension Analysis Using Mice Cine Mr Images

2021 ◽  
Author(s):  
Blanca Zufiria ◽  
Maialen Stephens ◽  
Maria Jesus Sanchez ◽  
Jesus Ruiz-Cabello ◽  
Karen Lopez-Linares ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Mr Images ◽  
Cardiac Segmentation ◽  
Cine Mr ◽  
Fully Automatic

SK-Unet: An Improved U-net Model with Selective Kernel for the Segmentation of LGE Cardiac MR Images

2021 ◽  
pp. 1-1
Author(s):  
Xiyue Wang ◽  
Sen Yang ◽  
Yuqi Fang ◽  
Yunpeng Wei ◽  
Minghui Wang ◽  
...  
Keyword(s):  
Mr Images ◽  
Cardiac Mr

scholarly journals Machine learning powered ellipsometry

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinchao Liu ◽  
Di Zhang ◽  
Dianqiang Yu ◽  
Mengxin Ren ◽  
Jingjun Xu
Keyword(s):  
Machine Learning ◽  
Optical Constants ◽  
Optical Characterization ◽  
Superior Performance ◽  
Trial And Error ◽  
Powerful Method ◽  
Human In The Loop ◽  
Ill Posed ◽  
Fully Automatic

AbstractEllipsometry is a powerful method for determining both the optical constants and thickness of thin films. For decades, solutions to ill-posed inverse ellipsometric problems require substantial human–expert intervention and have become essentially human-in-the-loop trial-and-error processes that are not only tedious and time-consuming but also limit the applicability of ellipsometry. Here, we demonstrate a machine learning based approach for solving ellipsometric problems in an unambiguous and fully automatic manner while showing superior performance. The proposed approach is experimentally validated by using a broad range of films covering categories of metals, semiconductors, and dielectrics. This method is compatible with existing ellipsometers and paves the way for realizing the automatic, rapid, high-throughput optical characterization of films.

Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images

2017 ◽  
pp. 73-82 ◽  
Author(s):  
Giacomo Tarroni ◽  
Ozan Oktay ◽  
Wenjia Bai ◽  
Andreas Schuh ◽  
Hideaki Suzuki ◽  
...  
Keyword(s):  
Short Axis ◽  
Short Axis Cine ◽  
Mr Images ◽  
Cardiac Mr ◽  
Coverage Estimation

scholarly journals TCT-2 Deep IVUS: A machine learning framework for fully automatic IVUS segmentation

2018 ◽  
Vol 72 (13) ◽  
pp. B1
Author(s):  
David Molony ◽  
Hossein Hosseini ◽  
Habib Samady
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Fully Automatic

Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach

2018 ◽  
Vol 12 (8) ◽  
pp. 1629-1637 ◽  
Author(s):  
Ahad Salimi ◽  
Mohammad Ali Pourmina ◽  
Mohammad-Shahram Moin
Keyword(s):  
Active Contour ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Fully Automatic
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