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Updated: Jul 10, 2026

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse
Published on: September 3, 2021
ICA for ovary tissue classification of perfusion magnetic resonance images
José J Rieta1, David Moratal, Luis Martí-Bonmatí
1Biomedical Synergy, Valencia University of Technology, Campus Gandia, 46730 Gandia, Valencia, Spain. jjrieta@ieee.org
Abstract:
In this study, a method to segment ovary Magnetic Resonance (MR) images and distinguish healthy tissue from cysts has been described. Through the application of independent component analysis (ICA) to a set of perfusion MR images it was possible to extract the output independent components and their corresponding signal-time curves. After examining and analyzing this result, a polynomial approach was computed to represent the main features of each curve, and automated particular selection of independent components was obtained by applying a Bayesian information criterion able to show the most relevant components. The results shown in this work permit to conclude that the independent components with a step-like signal-time curve allow to distinguish healthy tissue from cysts, thus, giving very promising results for the application of ICA to ovary tissue segmentation of perfusion MR images.
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