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.
© 2022 The Author(s)
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