Cargando…
Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation
Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebra...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669333/ https://www.ncbi.nlm.nih.gov/pubmed/34916898 http://dx.doi.org/10.3389/fnins.2021.755198 |
_version_ | 1784614757089673216 |
---|---|
author | Chen, Heping Shi, Yan Bo, Bin Zhao, Denghui Miao, Peng Tong, Shanbao Wang, Chunliang |
author_facet | Chen, Heping Shi, Yan Bo, Bin Zhao, Denghui Miao, Peng Tong, Shanbao Wang, Chunliang |
author_sort | Chen, Heping |
collection | PubMed |
description | Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%. |
format | Online Article Text |
id | pubmed-8669333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86693332021-12-15 Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation Chen, Heping Shi, Yan Bo, Bin Zhao, Denghui Miao, Peng Tong, Shanbao Wang, Chunliang Front Neurosci Neuroscience Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%. Frontiers Media S.A. 2021-11-30 /pmc/articles/PMC8669333/ /pubmed/34916898 http://dx.doi.org/10.3389/fnins.2021.755198 Text en Copyright © 2021 Chen, Shi, Bo, Zhao, Miao, Tong and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Chen, Heping Shi, Yan Bo, Bin Zhao, Denghui Miao, Peng Tong, Shanbao Wang, Chunliang Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title | Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title_full | Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title_fullStr | Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title_full_unstemmed | Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title_short | Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation |
title_sort | real-time cerebral vessel segmentation in laser speckle contrast image based on unsupervised domain adaptation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669333/ https://www.ncbi.nlm.nih.gov/pubmed/34916898 http://dx.doi.org/10.3389/fnins.2021.755198 |
work_keys_str_mv | AT chenheping realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT shiyan realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT bobin realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT zhaodenghui realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT miaopeng realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT tongshanbao realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation AT wangchunliang realtimecerebralvesselsegmentationinlaserspecklecontrastimagebasedonunsuperviseddomainadaptation |