WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy
The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morpholog...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Photoacoustics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221359792400017X |
| _version_ | 1797248339651067904 |
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| author | Jing Meng Jialing Yu Zhifeng Wu Fei Ma Yuanke Zhang Chengbo Liu |
| author_facet | Jing Meng Jialing Yu Zhifeng Wu Fei Ma Yuanke Zhang Chengbo Liu |
| author_sort | Jing Meng |
| collection | DOAJ |
| description | The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses. |
| first_indexed | 2024-04-24T20:13:01Z |
| format | Article |
| id | doaj.art-52a47e9377b74b6c9ebbce4bced8365c |
| institution | Directory Open Access Journal |
| issn | 2213-5979 |
| language | English |
| last_indexed | 2024-04-24T20:13:01Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj.art-52a47e9377b74b6c9ebbce4bced8365c2024-03-23T06:24:12ZengElsevierPhotoacoustics2213-59792024-06-0137100600WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopyJing Meng0Jialing Yu1Zhifeng Wu2Fei Ma3Yuanke Zhang4Chengbo Liu5School of Computer, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer, Qufu Normal University, Rizhao 276826, ChinaResearch Center for Biomedical Optics and Molecular Imaging, Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, ChinaSchool of Computer, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer, Qufu Normal University, Rizhao 276826, ChinaResearch Center for Biomedical Optics and Molecular Imaging, Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Corresponding author.The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses.http://www.sciencedirect.com/science/article/pii/S221359792400017XOptical-resolution photoacoustic microscopyMicrovascular extractionDeep learningHessian-matrix enhancement |
| spellingShingle | Jing Meng Jialing Yu Zhifeng Wu Fei Ma Yuanke Zhang Chengbo Liu WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy Photoacoustics Optical-resolution photoacoustic microscopy Microvascular extraction Deep learning Hessian-matrix enhancement |
| title | WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy |
| title_full | WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy |
| title_fullStr | WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy |
| title_full_unstemmed | WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy |
| title_short | WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy |
| title_sort | wsa mp net weak signal attention and multi scale perception network for microvascular extraction in optical resolution photoacoustic microcopy |
| topic | Optical-resolution photoacoustic microscopy Microvascular extraction Deep learning Hessian-matrix enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S221359792400017X |
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