Segmentation of Stimulated Raman Microscopy Images using a 1D Convolutional Neural Network

被引：2

作者：

Mozaffari, M. Hamed ^{[1
]}

Abdolghader, Pedram ^{[2
]}

Tay, Li-Lin ^{[3
]}

Stolow, Albert ^{[4
]}

机构：

[1] Natl Res Council Canada, Construct Res Ctr, Ottawa, ON, Canada

[2] Few Cycle Inc, Varennes, PQ, Canada

[3] Natl Res Council Canada, Metrol Res Ctr, Ottawa, ON, Canada

[4] Univ Ottawa, Dept Phys, Ottawa, ON, Canada

来源：

2022 PHOTONICS NORTH (PN) | 2022年

关键词：

coherent Raman microscopy; 1D CNN; hyperspectral image segmentation; deep learning; pattern recognition;

D O I：

10.1109/PN56061.2022.9908347

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Stimulated Raman Scattering (SRS) microscopy is a powerful nonlinear optical imaging technique deriving contrast from Raman active molecular vibrations. We demonstrate, using a supervised convolutional neural network (RM-Net), the creation of chemical maps from hyperspectral Stimulated Raman Scattering images. Using a limited number (800) of training spectra, the trained RM-Net model was successfully applied to new hyperspectral images without compromising accuracy.

引用

页数：1

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