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Marcio Soares Ferreira

dblp:426/5609 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 50% Bioinformatics and computational biology · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › biomedical signal processing
physiological signal analysis
0.812024
FEHAT: efficient, large scale and automated heartbeat detection in Medaka fish embryos · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

image segmentation · 0.8fourier transform · 0.8classification · 0.8
YearPublicationVenuePosition
2024 FEHAT: efficient, large scale and automated heartbeat detection in Medaka fish embryos
abstract
SUMMARY: High-resolution imaging of model organisms allows the quantification of important physiological measurements. In the case of fish with transparent embryos, these videos can visualize key physiological processes, such as heartbeat. High throughput systems can provide enough measurements for the robust investigation of developmental processes as well as the impact of system perturbations on physiological state. However, few analytical schemes have been designed to handle thousands of high-resolution videos without the need for some level of human intervention. We developed a software package, named FEHAT, to provide a fully automated solution for the analytics of large numbers of heart rate imaging datasets obtained from developing Medaka fish embryos in 96-well plate format imaged on an Acquifer machine. FEHAT uses image segmentation to define regions of the embryo showing changes in pixel intensity over time, followed by the classification of the most likely position of the heart and Fourier Transformations to estimate the heart rate. Here, we describe some important features of the FEHAT software, showcasing its performance across a large set of medaka fish embryos and compare its performance to established, less automated solutions. FEHAT provides reliable heart rate estimates across a range of temperature-based perturbations and can be applied to tens of thousands of embryos without the need for any human intervention. AVAILABILITY AND IMPLEMENTATION: Data used in this manuscript will be made available on request.
Marcio Soares Ferreira, Sebastian Stricker, Tomas W. Fitzgerald, Jack Monahan, Fanny Defranoux, Philip Watson, Bettina Welz, Omar Hammouda, Joachim Wittbrodt, Ewan Birney
Bioinform.1