Integrated optical matrix operator for biomedical image segmentation using deep residual U-Net

We present an on-chip optical matrix operator designed for multichannel 1×1 optical convolution, which is deployed in a deep residual U-Net for biomedical image segmentation. The integrated optical matrix operator is fabricated on a silicon-on-insulator (SOI) chip that supports fully reconfigurable 5×5 matrix operation and is used in a deep residual U-net that requires 3-channel 1×1 convolution operation. The multichannel optical 1×1 convolution operation is experimentally validated by using the deep residual U-Net to achieve precise segmentation of pneumonia lesion region in lung CT images. The on-chip optical matrix operator features high scalability and large-scale parallel operation and may find great application in future optical neural networks.

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Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1117/12.3047781
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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Integrated optical matrix operator for biomedical image segmentation using deep residual U-Net

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Neural Networks and Reservoir Computing
article

Integrated optical matrix operator for biomedical image segmentation using deep residual U-Net

Jiejun Zhang, Junyi Zhang, S.J. Dong, Yu Dong Huang, Jianping Yao, Chencheng Zeng
article en

Abstract

We present an on-chip optical matrix operator designed for multichannel 1×1 optical convolution, which is deployed in a deep residual U-Net for biomedical image segmentation. The integrated optical matrix operator is fabricated on a silicon-on-insulator (SOI) chip that supports fully reconfigurable 5×5 matrix operation and is used in a deep residual U-net that requires 3-channel 1×1 convolution operation. The multichannel optical 1×1 convolution operation is experimentally validated by using the deep residual U-Net to achieve precise segmentation of pneumonia lesion region in lung CT images. The on-chip optical matrix operator features high scalability and large-scale parallel operation and may find great application in future optical neural networks.

University of Ottawa (CA)
Openalex Percentile: Top 79%
Neural Networks and Reservoir Computing
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