Literature DB >> 31536467

Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Mark Schutera1, Steffen Just2, Jakob Gierten3,4, Ralf Mikut1, Markus Reischl1, Christian Pylatiuk1.   

Abstract

Medaka (Oryzias latipes) and zebrafish (Danio rerio) contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have developed a framework that segments the ventricle of a medaka hatchling from image sequences and subsequently quantifies ventricular dimensions.

Entities:  

Keywords:  biomedical imaging; deep learning; fractional shortening; medaka; segmentation; zebrafish

Mesh:

Year:  2019        PMID: 31536467     DOI: 10.1089/zeb.2019.1754

Source DB:  PubMed          Journal:  Zebrafish        ISSN: 1545-8547            Impact factor:   1.985


  2 in total

1.  Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels.

Authors:  Mark Schutera; Luca Rettenberger; Christian Pylatiuk; Markus Reischl
Journal:  PLoS One       Date:  2022-02-08       Impact factor: 3.240

2.  Cytotoxic Evaluation and Anti-Angiogenic Effects of Two Furano-Sesquiterpenoids from Commiphora myrrh Resin.

Authors:  Ali S Alqahtani; Fahd A Nasr; Omar M Noman; Muhammad Farooq; Tariq Alhawassi; Wajhul Qamar; Ali El-Gamal
Journal:  Molecules       Date:  2020-03-13       Impact factor: 4.411

  2 in total

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