O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry

Abstract

Estimation of blood oxygenation with spectroscopic photoacoustic imaging is a promising tool for several biomedical applications. For this method to be quantitative, it relies on an accurate method of the light fluence in the tissue. This is difficult deep in heterogeneous tissue, where different wavelengths of light can experience significantly different attenuation. In this work, we developed a new deep neural network to simultaneously estimate the oxygen saturation in blood vessels and segment the vessels from the surrounding background tissue. The network was trained on estimated initial pressure distributions from three-dimensional Monte Carlo simulations of light transport in breast tissue. The network estimated vascular SO2 in less than 50 ms with as little as 5.1% median error and better than 95% segmentation accuracy. Overall, these results show that the blood oxygenation can be quantitatively mapped in real-time with high accuracy.

References in corpus (2)

• Context encoding enables machine learning-based quantitative photoacoustics
• Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo

Cited by in corpus (5)

• Deep Learning in Photoacoustic Tomography: Current approaches and future directions
• Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
• Semantic segmentation of multispectral photoacoustic images using deep learning
• Deep learning for photoacoustic imaging: a survey
• Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D