Deep learning for photoacoustic imaging: a survey

Abstract

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.

arXiv commentA review of deep learning for photoacoustic imaging

References in corpus (20)

• A Survey on Deep Learning in Medical Image Analysis
• An overview of gradient descent optimization algorithms
• Striving for Simplicity: The All Convolutional Net
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Wide Residual Networks
• Recent Trends in Deep Learning Based Natural Language Processing
• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
• The Microsoft 2017 Conversational Speech Recognition System
• Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
• Dive into Deep Learning
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties
• O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry
• Real-time photoacoustic projection imaging using deep learning
• Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography
• Deep learning versus ${\ell }^1$-minimization for compressed sensing photoacoustic tomography
• Reconstructing undersampled photoacoustic microscopy images using deep learning
• Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D
• Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging

Cited by in corpus (4)

• Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction
• Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint
• A Jointed Feature Fusion Framework for Photoacoustic Reconstruction
• Importance of Signal and Image Processing in Photoacoustic Imaging