Toward accurate quantitative photoacoustic imaging : learning vascular blood oxygen saturation in three dimensions
Bench, Ciaran; Hauptmann, Andreas; Cox, Ben (2020-08-24)
Ciaran Bench, Andreas Hauptmann, and Ben T. Cox "Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions," Journal of Biomedical Optics 25(8), 085003 (24 August 2020). https://doi.org/10.1117/1.JBO.25.8.085003
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https://urn.fi/URN:NBN:fi-fe2020082663168
Tiivistelmä
Abstract
Significance:Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO₂ from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images.
Aim:To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO₂ from realistic tissue models/images.
Approach:Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models.
Results:The mean of the absolute difference between the true mean vessel sO₂ and the network output for 40 examples was 4.4% and the standard deviation was 4.5%.
Conclusions:3-D fully convolutional networks were shown capable of producing accurate sO₂ maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.
Kokoelmat
- Avoin saatavuus [31989]