At present, approximately one third of adults worldwide are obese or overweight. The phenomenon of adipose browning, that is, adipocyte trans-differentiation from an energy storage role to a heat production role, opens up a new way to reverse obesity. However, current methods for judging whether adipose browning occurs are usually based on invasive histological assessments. Adipose browning has been found to be accompanied by changes in adipose microvascular system. Therefore, our research focused on the above biological feature and used a non-invasive photoacoustic imaging technique that integrates the advantages of optical and acoustic imaging to visualize adipose microvascular system in obese mice treated with cold stimulation in a three-dimensional mode. The results showed that based on hemoglobin components, photoacoustic molecular imaging could not only accurately visually display a full view of increased arteriovenous blood network and enhanced blood oxygen consumption during adipose browning, but also quantitatively analyze it. Moreover, the data was consistent with the results of histological means including hematoxylin-eosin staining and immunofluorescence staining. In summary, we demonstrated the feasibility of photoacoustic molecular imaging for detecting adipose browning. This work will provide a new possibility for non-invasive assessment of adipose browning, which can help researchers and clinicians fight against obesity.
{"title":"Photoacoustic molecular imaging for microvascular system in browning adipose tissues","authors":"Ronghe Chen, Sangni Lixu, Tao Chen","doi":"10.1117/12.2681769","DOIUrl":"https://doi.org/10.1117/12.2681769","url":null,"abstract":"At present, approximately one third of adults worldwide are obese or overweight. The phenomenon of adipose browning, that is, adipocyte trans-differentiation from an energy storage role to a heat production role, opens up a new way to reverse obesity. However, current methods for judging whether adipose browning occurs are usually based on invasive histological assessments. Adipose browning has been found to be accompanied by changes in adipose microvascular system. Therefore, our research focused on the above biological feature and used a non-invasive photoacoustic imaging technique that integrates the advantages of optical and acoustic imaging to visualize adipose microvascular system in obese mice treated with cold stimulation in a three-dimensional mode. The results showed that based on hemoglobin components, photoacoustic molecular imaging could not only accurately visually display a full view of increased arteriovenous blood network and enhanced blood oxygen consumption during adipose browning, but also quantitatively analyze it. Moreover, the data was consistent with the results of histological means including hematoxylin-eosin staining and immunofluorescence staining. In summary, we demonstrated the feasibility of photoacoustic molecular imaging for detecting adipose browning. This work will provide a new possibility for non-invasive assessment of adipose browning, which can help researchers and clinicians fight against obesity.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123713423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cherenkov-Excited Luminescence Scanned Tomography (CELST) is a new emerging imaging modality, which uses the Cherenkov light to excite fluorophores for tomographic imaging. In order to improve the imaging depth and spatial resolution, a rotational CELST was developed to scan the imaging object to produce sinogram data, and a Filtered Back Projection (FBP) was used to recover the distribution of fluorophores. However, the images reconstructed by FBP are usually corrupted by artifacts due to measurements from limited angles. To reduce the artifacts, we propose a deep learning-based reconstruction algorithm (SAM-Unet), which is based on a fully convolutional deep neural network with U-Net structure, and a spatial attention module was added between the encoder and the decoder. The image features extracted by the spatial attention module are transferred to the decoder through a skip connection structure. The effectiveness of the proposed SAM-Unet is verified by numerical experiments, and the results show that the SAM-Unet can improve the mean square error (MSE) (97.5%), Peak Signal-To-Noise Ratio (PSNR) (81.9%) and Structure Similarity Index Measure (SSIM) (63.4%) compared with the FBP algorithm. Compared with the deep learning method U-Net, the MSE improved 39.8%, the PSNR improved 8.0% and SSIM improved 2.6%.
{"title":"Limited-angle cherenkov-excited luminescence scanned tomography reconstruction based on spatial attention module","authors":"Mengfan Geng, Hu Zhang, Jingyue Zhang, Kebin Jia, Zhonghua Sun, Zhe Li, Jinchao Feng","doi":"10.1117/12.2682920","DOIUrl":"https://doi.org/10.1117/12.2682920","url":null,"abstract":"Cherenkov-Excited Luminescence Scanned Tomography (CELST) is a new emerging imaging modality, which uses the Cherenkov light to excite fluorophores for tomographic imaging. In order to improve the imaging depth and spatial resolution, a rotational CELST was developed to scan the imaging object to produce sinogram data, and a Filtered Back Projection (FBP) was used to recover the distribution of fluorophores. However, the images reconstructed by FBP are usually corrupted by artifacts due to measurements from limited angles. To reduce the artifacts, we propose a deep learning-based reconstruction algorithm (SAM-Unet), which is based on a fully convolutional deep neural network with U-Net structure, and a spatial attention module was added between the encoder and the decoder. The image features extracted by the spatial attention module are transferred to the decoder through a skip connection structure. The effectiveness of the proposed SAM-Unet is verified by numerical experiments, and the results show that the SAM-Unet can improve the mean square error (MSE) (97.5%), Peak Signal-To-Noise Ratio (PSNR) (81.9%) and Structure Similarity Index Measure (SSIM) (63.4%) compared with the FBP algorithm. Compared with the deep learning method U-Net, the MSE improved 39.8%, the PSNR improved 8.0% and SSIM improved 2.6%.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122427629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuo Wang, Aishi Wang, Qiao Liu, Shuanglian Wang, Xuantao Su
Liver cancer is one of the most common digestive system malignancies with an average five-year survival rate of less than 20%, while traditional methods are often unautomated, labeling required, and limited for early liver cancer detection. Exosomes are a type of extracellular vesicles with a diameter of 40-150 nm, which play important role in disease diagnosis and treatment. It is of interest to develop a label-free optical system for the analysis of nanoscale exosomes. Here, we developed a label-free two-dimensional (2D) light scattering acquisition system for the measurements of microparticles and the exosomes derived from the normal liver cells. By adjusting the thickness of the light sheet for illumination in our system, nanoparticles down to 41 nm are detected. The visualization and accurate particle size analysis of liver cell exosomes are then performed by our 2D light scattering technology. Our method is expected to have important applications in the quantitative analysis field of cellular and extracellular structures that may find potential applications in clinics such as for early cancer diagnosis.
{"title":"Label-free analysis of single microparticles and nanoscale exosomes with two-dimensional light scattering technology","authors":"Zhuo Wang, Aishi Wang, Qiao Liu, Shuanglian Wang, Xuantao Su","doi":"10.1117/12.2682909","DOIUrl":"https://doi.org/10.1117/12.2682909","url":null,"abstract":"Liver cancer is one of the most common digestive system malignancies with an average five-year survival rate of less than 20%, while traditional methods are often unautomated, labeling required, and limited for early liver cancer detection. Exosomes are a type of extracellular vesicles with a diameter of 40-150 nm, which play important role in disease diagnosis and treatment. It is of interest to develop a label-free optical system for the analysis of nanoscale exosomes. Here, we developed a label-free two-dimensional (2D) light scattering acquisition system for the measurements of microparticles and the exosomes derived from the normal liver cells. By adjusting the thickness of the light sheet for illumination in our system, nanoparticles down to 41 nm are detected. The visualization and accurate particle size analysis of liver cell exosomes are then performed by our 2D light scattering technology. Our method is expected to have important applications in the quantitative analysis field of cellular and extracellular structures that may find potential applications in clinics such as for early cancer diagnosis.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wangbiao Li, Ke Li, Dezi Li, Haiyu Chen, Wenliang Cao, Shufeng Zhuo, Hui Li, Zhifang Li
In this study, the modified spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) is proposed for determining the birefringence of the myocardial tissue. In this modified SD PS-OCT, the circular polarization state of light was generated before entering the beam splitter. Thus, the polarization states in the reference and sample arms are both circular, and the symmetry between them is good without using additional Quarter-Wave Plate (QWP), which reduce the dispersion effect. The results demonstrated that theoretical analysis for determination of birefringence including the phase retardance and the fast axis orientation based on Stokes parameters of backscattered from biological tissue, which is different from the traditional SD PS-OCT. In addition, the phase retardance and the fast axis orientation was used to differentiate the myocaridal tissue in the diastole of the cardiac cycle the from that in the systole of the cardiac cycle. The findings suggest that the SD PS-OCT be a potential tool for the real-time monitoring the change of the myocardial wall during the cardiac cycle.
{"title":"Determination of birefringence of myocardial tissue based on PS-OCT with circularly polarized light as reference","authors":"Wangbiao Li, Ke Li, Dezi Li, Haiyu Chen, Wenliang Cao, Shufeng Zhuo, Hui Li, Zhifang Li","doi":"10.1117/12.2683117","DOIUrl":"https://doi.org/10.1117/12.2683117","url":null,"abstract":"In this study, the modified spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) is proposed for determining the birefringence of the myocardial tissue. In this modified SD PS-OCT, the circular polarization state of light was generated before entering the beam splitter. Thus, the polarization states in the reference and sample arms are both circular, and the symmetry between them is good without using additional Quarter-Wave Plate (QWP), which reduce the dispersion effect. The results demonstrated that theoretical analysis for determination of birefringence including the phase retardance and the fast axis orientation based on Stokes parameters of backscattered from biological tissue, which is different from the traditional SD PS-OCT. In addition, the phase retardance and the fast axis orientation was used to differentiate the myocaridal tissue in the diastole of the cardiac cycle the from that in the systole of the cardiac cycle. The findings suggest that the SD PS-OCT be a potential tool for the real-time monitoring the change of the myocardial wall during the cardiac cycle.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124821505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Photoacoustic imaging is an imaging technology which combines the advantages of high-resolution optical imaging and deep detection depth of acoustic imaging. Photoacoustic imaging combined with hysteroscopy may be a new diagnostic technique for endometrial cancer. However, the energy loss after pulsed laser passing through the hysteroscope is very large. Therefore, the energy of pulsed laser after hysteroscopy based on photoacoustic imaging is worth further discussion. A coupling Program of pulsed laser and hysteroscope based on the optical path of pulsed laser and hysteroscope was designed in this paper. The Program was optimized by ZEMAX simulation, and then the optimal effect of pulsed laser observation through hysteroscopy was verified by phantom experiment. The results show that the pulsed laser can obtain better photoacoustic signals after passing through our coupling module. This method is expected to be applied to the detection of endometrial diseases in clinic.
{"title":"Optimization design of the coupling scheme of the pulse laser output through the hysteroscope observation channel","authors":"Jinrui Wang, Chuanhui Ge, Guansong Zou, Feiyang Wang, Xiaoman Zhang, Shulian Wu, Hengchang Guo, Huan Yi, Yulong Zhang, Hui Li","doi":"10.1117/12.2682773","DOIUrl":"https://doi.org/10.1117/12.2682773","url":null,"abstract":"Photoacoustic imaging is an imaging technology which combines the advantages of high-resolution optical imaging and deep detection depth of acoustic imaging. Photoacoustic imaging combined with hysteroscopy may be a new diagnostic technique for endometrial cancer. However, the energy loss after pulsed laser passing through the hysteroscope is very large. Therefore, the energy of pulsed laser after hysteroscopy based on photoacoustic imaging is worth further discussion. A coupling Program of pulsed laser and hysteroscope based on the optical path of pulsed laser and hysteroscope was designed in this paper. The Program was optimized by ZEMAX simulation, and then the optimal effect of pulsed laser observation through hysteroscopy was verified by phantom experiment. The results show that the pulsed laser can obtain better photoacoustic signals after passing through our coupling module. This method is expected to be applied to the detection of endometrial diseases in clinic.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mengjia Xue, Tianrui Liu, Yizhen Xie, Meiya Dong, Xiuping Liu
Three-dimensional dose prediction is an important step in automatic radiotherapy planning. Using deep learning combined with Knowledge-Based Planning methods (KBP) can achieve dose distribution prediction on CT images. Convolutional Neural Networks (CNN) are an important branch of deep learning algorithms. This article will briefly introduce the application of convolutional neural networks and other advanced algorithm structures in dose prediction. And there are different studies that evaluate the model output results by studying different transformation models, different patient data and data of different treatment methods, and find the optimal dose prediction model. However, the research on dose prediction models is not the most complete. There is still room for further research in terms of input tumor types, treatment methods, etc. Moreover, automatic radiotherapy plan generation is the ultimate goal, and further research is needed.
{"title":"A deep learning-based algorithm for three-dimensional dose prediction","authors":"Mengjia Xue, Tianrui Liu, Yizhen Xie, Meiya Dong, Xiuping Liu","doi":"10.1117/12.2685094","DOIUrl":"https://doi.org/10.1117/12.2685094","url":null,"abstract":"Three-dimensional dose prediction is an important step in automatic radiotherapy planning. Using deep learning combined with Knowledge-Based Planning methods (KBP) can achieve dose distribution prediction on CT images. Convolutional Neural Networks (CNN) are an important branch of deep learning algorithms. This article will briefly introduce the application of convolutional neural networks and other advanced algorithm structures in dose prediction. And there are different studies that evaluate the model output results by studying different transformation models, different patient data and data of different treatment methods, and find the optimal dose prediction model. However, the research on dose prediction models is not the most complete. There is still room for further research in terms of input tumor types, treatment methods, etc. Moreover, automatic radiotherapy plan generation is the ultimate goal, and further research is needed.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131226260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semi-automatic segmentation of coronary vessels in Digital Subtraction Angiography (DSA) images is more practical than fully automatic segmentation. In this paper, a novel algorithm is proposed to extract coronary vessels semi-automatically. This algorithm is a combination of the traditional livewire algorithm and the gradient vector flow-Frangi (GVF-Frangi) filter. Results showed that the proposed approach is more robust and effective in the semi-automatic segmentation of coronary vessels than the traditional livewire algorithm.
{"title":"Semi-automatic segmentation of coronary vessels based on improved livewire algorithm","authors":"Jessica Dong, Zhengguo Dai, Tianwu Xie, Xin Yi","doi":"10.1117/12.2683104","DOIUrl":"https://doi.org/10.1117/12.2683104","url":null,"abstract":"Semi-automatic segmentation of coronary vessels in Digital Subtraction Angiography (DSA) images is more practical than fully automatic segmentation. In this paper, a novel algorithm is proposed to extract coronary vessels semi-automatically. This algorithm is a combination of the traditional livewire algorithm and the gradient vector flow-Frangi (GVF-Frangi) filter. Results showed that the proposed approach is more robust and effective in the semi-automatic segmentation of coronary vessels than the traditional livewire algorithm.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131482613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duan Chen, Ning Li, Xiuli Liu, Shaoqun Zeng, Xiaohua Lv, Li Chen, Yu-Xiang Xiao, Qinglei Hu
Blood analysis, through the complete blood count, remains the most fundamental medical test for diagnosing broad diseases. Even so, it is still limited to central laboratories with sophisticated facilities and skilled professionals. Here, we propose a simple, machine-vision-aided, label-free blood analysis technique via a regular microscope utilizing contrast-enhance defocus imaging, i.e., defocusing imaging under 415 nm, small aperture illumination. We have shown that this technique can simultaneously obtain leucocytes’ optical phase and erythrocytes’ spectrophotometric information, making it feasible to realize five-part leucocyte differential and hemoglobin quantification with machine vision. The reliability was verified by comparing the quantified results with clinical reference results, which indicates significant linear correlations (significance levels ⪅ 0.0001 and Pearson coefficients ⪆ 0.90). We also show that the virtual staining of the label-free blood cell images can be performed with a generative adversarial network to mimic conventional Wright-Giemsa images, facilitating this technique’s medical translation. This study reports a simple, easy-to-use, quick, reliable blood analysis technique that may lead to a reformation in the blood analysis field. We emphasize this technique’s great potential for early screening of various diseases, including anemia, leukemia, and neglected tropical diseases, especially in resource-limited settings.
{"title":"Label-free blood analysis utilizing contrast-enhanced defocusing imaging with machine vision","authors":"Duan Chen, Ning Li, Xiuli Liu, Shaoqun Zeng, Xiaohua Lv, Li Chen, Yu-Xiang Xiao, Qinglei Hu","doi":"10.1117/12.2682246","DOIUrl":"https://doi.org/10.1117/12.2682246","url":null,"abstract":"Blood analysis, through the complete blood count, remains the most fundamental medical test for diagnosing broad diseases. Even so, it is still limited to central laboratories with sophisticated facilities and skilled professionals. Here, we propose a simple, machine-vision-aided, label-free blood analysis technique via a regular microscope utilizing contrast-enhance defocus imaging, i.e., defocusing imaging under 415 nm, small aperture illumination. We have shown that this technique can simultaneously obtain leucocytes’ optical phase and erythrocytes’ spectrophotometric information, making it feasible to realize five-part leucocyte differential and hemoglobin quantification with machine vision. The reliability was verified by comparing the quantified results with clinical reference results, which indicates significant linear correlations (significance levels ⪅ 0.0001 and Pearson coefficients ⪆ 0.90). We also show that the virtual staining of the label-free blood cell images can be performed with a generative adversarial network to mimic conventional Wright-Giemsa images, facilitating this technique’s medical translation. This study reports a simple, easy-to-use, quick, reliable blood analysis technique that may lead to a reformation in the blood analysis field. We emphasize this technique’s great potential for early screening of various diseases, including anemia, leukemia, and neglected tropical diseases, especially in resource-limited settings.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131931159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hong Peng, N. Niu, Yilin Zhang, Guanjun Wang, Chenyang Xue, Mengxing Huang
Image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network,merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features.
{"title":"Application of image segmentation technology in TCM eye diagnosis","authors":"Hong Peng, N. Niu, Yilin Zhang, Guanjun Wang, Chenyang Xue, Mengxing Huang","doi":"10.1117/12.2679810","DOIUrl":"https://doi.org/10.1117/12.2679810","url":null,"abstract":"Image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network,merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Light scattering flow cytometry has been demonstrated for label-free particle and cell analysis. The high-throughput imaging of cells or particles in flow cytometry is fundamentally challenging as motion blur may occur for weak-light measurements. Here we perform light scattering measurements on 3.87 μm and 4.19 μm microparticles in diameters with our light scattering flow cytometer. Two-dimensional (2D) light scattering patterns are imaged by a Complementary Metal Oxide Semiconductor (CMOS) sensor. The hydrodynamic focusing effect is studied for better high-throughput measurements. Motion-blur images are obtained at 2.4 mm/s flow rate with 10 ms exposure time, and a deblurring algorithm is adopted for analysis. The experimental 2D light scattering patterns agree well with the Mie theory simulation. Moreover, the number of 3.87 μm and 4.19 μm microparticles in flow can be determined, where the error is less than 5% compared with the theoretical results.
{"title":"Processing and analysis of motion-blur images in light scattering flow cytometry","authors":"F. Yuan, Zhiwen Wang, Qiao Liu, Xuantao Su","doi":"10.1117/12.2682917","DOIUrl":"https://doi.org/10.1117/12.2682917","url":null,"abstract":"Light scattering flow cytometry has been demonstrated for label-free particle and cell analysis. The high-throughput imaging of cells or particles in flow cytometry is fundamentally challenging as motion blur may occur for weak-light measurements. Here we perform light scattering measurements on 3.87 μm and 4.19 μm microparticles in diameters with our light scattering flow cytometer. Two-dimensional (2D) light scattering patterns are imaged by a Complementary Metal Oxide Semiconductor (CMOS) sensor. The hydrodynamic focusing effect is studied for better high-throughput measurements. Motion-blur images are obtained at 2.4 mm/s flow rate with 10 ms exposure time, and a deblurring algorithm is adopted for analysis. The experimental 2D light scattering patterns agree well with the Mie theory simulation. Moreover, the number of 3.87 μm and 4.19 μm microparticles in flow can be determined, where the error is less than 5% compared with the theoretical results.","PeriodicalId":110373,"journal":{"name":"International Conference on Photonics and Imaging in Biology and Medicine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116872622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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