Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images
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Date
2021-03
Publication Type
Journal Article
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yes
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Abstract
The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics.
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published
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Volume
68 (3)
Pages / Article No.
688 - 696
Publisher
IEEE
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Subject
Convolutional neural networks (CNNs); deep learning (DL) for image segmentation; optoacoustic imaging; semantic segmentation
Organisational unit
09648 - Razansky, Daniel / Razansky, Daniel