An efficient deep learning segmentation scheme for cervical collagen and elastin quantification in Mueller matrix polarimetry microscopic images

Nelson Gary; Vinh Nguyen Du Le; Vinh Nguyen Du Le; Julien Wojak; Mouloud Adel; Jessica Ramella-Roman; Jessica Ramella-Roman; Anabela Da Silva

doi:10.1364/TRANSLATIONAL.2022.TM4B.2

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper TM4B.2
•https://doi.org/10.1364/TRANSLATIONAL.2022.TM4B.2

An efficient deep learning segmentation scheme for cervical collagen and elastin quantification in Mueller matrix polarimetry microscopic images

Nelson Gary, Vinh Nguyen Du Le, Julien Wojak, Mouloud Adel, Jessica Ramella-Roman, and Anabela Da Silva

Clinical and Translational Biophotonics 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Women's Health (TM4B)

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N. Gary, V. N. D. Le, J. Wojak, M. Adel, J. Ramella-Roman, and A. D. Silva, "An efficient deep learning segmentation scheme for cervical collagen and elastin quantification in Mueller matrix polarimetry microscopic images," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper TM4B.2.

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Abstract

We present an efficient deep learning-based segmentation approach able to discriminate collagen from elastic fibers from Mueller matrix microscopy images of the mouse cervix. Thanks to the use of a Self-Validating Mueller matrix Micro-Mesoscope (SAMMM) system, Second Harmonic Generation (SHG) and Two Photon Excitation Fluorescence (TPEF) are also acquired and used as the quantitative segmentation ground truth. The method combines a multilayer perceptron and two U-net convolution networks. The accuracy and image quality metrics show improved segmentation results.

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