Deep learning for photoacoustic tomography from sparse data.
TLDR
In this article, the sparse data problem for image reconstruction in photoacousti... is investigated and a fast and accurate image reconstruction algorithm is proposed for computed tomography with sparse data.Abstract:
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacousti...read more
Citations
More filters
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal
TL;DR: A modified convolutional neural network architecture termed fully dense UNet (FD-UNet) is proposed for removing artifacts from two-dimensional PAT images reconstructed from sparse data and the proposed CNN is compared with the standard UNet in terms of reconstructed image quality.
Journal ArticleDOI
Deep learning for tomographic image reconstruction
TL;DR: Wang et al. as mentioned in this paper provided a general background, highlighted representative results with an emphasis on medical imaging, and discussed key issues that need to be addressed in this emerging field.
Journal ArticleDOI
One-step robust deep learning phase unwrapping.
TL;DR: With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a singledeep neural network.
Journal ArticleDOI
Deep learning optoacoustic tomography with sparse data
Neda Davoudi,Neda Davoudi,Xosé Luís Deán-Ben,Xosé Luís Deán-Ben,Daniel Razansky,Daniel Razansky +5 more
TL;DR: A new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network is proposed and its performance with whole body mouse imaging in vivo is demonstrated.
References
More filters
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.