Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains

Mathematical Morphology

Jenett Arnim¹  Simion Matthieu²  Lempereur Sylvain³  Machado Elodie⁴  Arganda-Carreras Ignacio⁴  Talbot Hugues⁴  Affaticati Pierre⁴  Joly Jean-Stéphane⁴ 

[1] Tefor Paris-Saclay, UMS2010, CNRS/INRA, Université Paris-Saclay, Gif sur Yvette, France;Ikerbasque, Basque Foundation for Science, Bilbao, Spain, Computer Science and Artificial Intelligence Department, University of the Basque Country, San Sebastian, Spai, Donostia International Physics Center, San Sebastian, Spain;LIGM, Univ Gustave Eiffel, CNRS, ESIEE Paris, F-77454 Marne-la-Vallée, France,;Tefor Paris-Saclay, UMS2010, CNRS/INRA, Université Paris-Saclay, Gif sur Yvette, France;

关键词: contrast correction;    image restoration;    light attenuation;    registration;    high-content screening;    watershed segmentation;    68u10;    92c55;   

DOI  :  10.1515/mathm-2020-0100

来源: DOAJ

【 摘 要 】

Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible.

【 授权许可】

Unknown