High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance
Laser speckle contrast imaging (LSCI) is a noninvasive, label-free technique that allows real-time investigation of the microcirculation situation of biological tissue. High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics. Howev...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2025-01-01
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| Series: | Journal of Innovative Optical Health Sciences |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545824500159 |
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| author | Shenglan Yao Huiling Wu Suzhong Fu Shuting Ling Kun Wang Hongqin Yang Yaqin He Xiaolan Ma Xiaofeng Ye Xiaofei Wen Qingliang Zhao |
| author_facet | Shenglan Yao Huiling Wu Suzhong Fu Shuting Ling Kun Wang Hongqin Yang Yaqin He Xiaolan Ma Xiaofeng Ye Xiaofei Wen Qingliang Zhao |
| author_sort | Shenglan Yao |
| collection | DOAJ |
| description | Laser speckle contrast imaging (LSCI) is a noninvasive, label-free technique that allows real-time investigation of the microcirculation situation of biological tissue. High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics. However, achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology. In addition, supervised learning methods heavily rely on high-quality labels for accurate segmentation results, which often necessitate extensive labeling efforts. Here, we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing. Furthermore, we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets, even when confronted with unfamiliar data. Importantly, the dice similarity coefficient exceeded 85% in a rat experiment. Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations. This would greatly benefit future research in life and medicine. |
| format | Article |
| id | doaj-art-ff698c3fbeb440e29798b396d0f26a36 |
| institution | Kabale University |
| issn | 1793-5458 1793-7205 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj-art-ff698c3fbeb440e29798b396d0f26a362025-01-27T05:49:52ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052025-01-01180110.1142/S1793545824500159High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label relianceShenglan Yao0Huiling Wu1Suzhong Fu2Shuting Ling3Kun Wang4Hongqin Yang5Yaqin He6Xiaolan Ma7Xiaofeng Ye8Xiaofei Wen9Qingliang Zhao10State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350117, P. R. ChinaThe Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350117, P. R. ChinaDepartment of Oncology Surgery, General Hospital of Ningxia Medical University, Yinchuan 750004, P. R. ChinaDepartment of Oncology Surgery, General Hospital of Ningxia Medical University, Yinchuan 750004, P. R. ChinaDepartment of Oncology Surgery, General Hospital of Ningxia Medical University, Yinchuan 750004, P. R. ChinaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Department of Vascular & Tumor Interventional Radiology, The First Affiliated Hospital of Xiamen University, School of Medicine, School of Public Health, Xiamen University, Xiamen 361102, P. R. ChinaLaser speckle contrast imaging (LSCI) is a noninvasive, label-free technique that allows real-time investigation of the microcirculation situation of biological tissue. High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics. However, achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology. In addition, supervised learning methods heavily rely on high-quality labels for accurate segmentation results, which often necessitate extensive labeling efforts. Here, we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing. Furthermore, we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets, even when confronted with unfamiliar data. Importantly, the dice similarity coefficient exceeded 85% in a rat experiment. Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations. This would greatly benefit future research in life and medicine.https://www.worldscientific.com/doi/10.1142/S1793545824500159Biomedical imaginglaser speckle contrast imagingvessel segmentationweakly supervised learningmicrocirculation |
| spellingShingle | Shenglan Yao Huiling Wu Suzhong Fu Shuting Ling Kun Wang Hongqin Yang Yaqin He Xiaolan Ma Xiaofeng Ye Xiaofei Wen Qingliang Zhao High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance Journal of Innovative Optical Health Sciences Biomedical imaging laser speckle contrast imaging vessel segmentation weakly supervised learning microcirculation |
| title | High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance |
| title_full | High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance |
| title_fullStr | High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance |
| title_full_unstemmed | High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance |
| title_short | High-performance laser speckle contrast image vascular segmentation without delicate pseudo-label reliance |
| title_sort | high performance laser speckle contrast image vascular segmentation without delicate pseudo label reliance |
| topic | Biomedical imaging laser speckle contrast imaging vessel segmentation weakly supervised learning microcirculation |
| url | https://www.worldscientific.com/doi/10.1142/S1793545824500159 |
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