Conference on Biomedical Photonics and Cross-Fusion最新文献_第2页

Evaluating tissue mechanical properties using Mueller matrix polarimetry 用穆勒矩阵偏振法评价组织力学性能

Conference on Biomedical Photonics and Cross-Fusion Pub Date : 2023-07-28 DOI: 10.1117/12.2691003

Jiahao Fan, Honghui He, Hui Ma

{"title":"Evaluating tissue mechanical properties using Mueller matrix polarimetry","authors":"Jiahao Fan, Honghui He, Hui Ma","doi":"10.1117/12.2691003","DOIUrl":"https://doi.org/10.1117/12.2691003","url":null,"abstract":"Evaluating tissue mechanical properties is an important issue in the biomedical field. While traditional in vitro tissue deformation experiments have been used to measure mechanical properties, optical methods are becoming increasingly popular due to their non-invasive and non-contact advantages. In this study, we utilized Mueller matrix polarimetry to quantify the mechanical properties of bovine tendon tissue. We acquired 3×3 Mueller matrix images of the tendon tissue samples under various stretching states using a backscattering measurement setup based on a polarization camera, enabling us to examine changes in both structural information and optical properties. Subsequently, we extracted frequency distribution histograms of Mueller matrix elements to elucidate the structural changes in the tendon tissue during the stretching process. We then calculated the Mueller matrix transformation parameters, namely the total anisotropy t1 and anisotropy direction α1 of the tendon tissue samples under different stretching processes, to characterize their structural changes quantitatively. For better discrimination of tendon tissues under different stretching states, we trained an image classification neural network using the derived MMT parameters as input. Ultimately, we obtained a highly accurate model with 90% precision. The results demonstrate the potential of Mueller matrix polarimetry as a tool for evaluating tissue mechanical properties.","PeriodicalId":164997,"journal":{"name":"Conference on Biomedical Photonics and Cross-Fusion","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713728","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}

引用次数: 0

Multifunctional optical tomography system combining surface extraction and 3D fluorescence reconstruction 结合表面提取和三维荧光重建的多功能光学层析成像系统

Conference on Biomedical Photonics and Cross-Fusion Pub Date : 2023-07-28 DOI: 10.1117/12.2692026

Yanan Wu, Shengyu Gao, Linlin Li, Jianru Zhang, Qian Hu, Xin Lou, Xinjun Zhu, Jiahua Jiang, Wuwei Ren

引用次数: 1

Study on the influencing factors of structured light digital holography 结构光数字全息的影响因素研究

Conference on Biomedical Photonics and Cross-Fusion Pub Date : 2023-07-28 DOI: 10.1117/12.2691140

Longyun Zhu, Y. Xu, Jialing Huang, Yining Wang, Zhicong Li, Ya-Wei Wang

引用次数: 0

Multi-functional accessory toolkit for Miniscope prototyping and image enhancement 多功能配件工具包迷你镜原型和图像增强

Conference on Biomedical Photonics and Cross-Fusion Pub Date : 2023-07-28 DOI: 10.1117/12.2691914

Xinyi Zhu, Liang Gu, Ruiping Li, Liang Chen, Jingying Chen, Ning Zhou, Wuwei Ren

引用次数: 0

Iterative-in-iterative super-resolution biomedical imaging using one real image 使用一张真实图像进行迭代的超分辨率生物医学成像

Conference on Biomedical Photonics and Cross-Fusion Pub Date : 2023-06-26 DOI: 10.1117/12.2691281

Yuanzheng Ma, Xinyue Wang, Benqi Zhao, Ying Xiao, Shijie Deng, Jian Song, Xun Guan

{"title":"Iterative-in-iterative super-resolution biomedical imaging using one real image","authors":"Yuanzheng Ma, Xinyue Wang, Benqi Zhao, Ying Xiao, Shijie Deng, Jian Song, Xun Guan","doi":"10.1117/12.2691281","DOIUrl":"https://doi.org/10.1117/12.2691281","url":null,"abstract":"Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the requirement of an extensive collection of high-resolution images presents limitations for widespread adoption in clinical practice. In our experiment, we proposed an approach to effectively train the deep learning-based super-resolution models using only one real image by leveraging self-generated high resolution images. We employed a mixed metric of image screening to automatically select images with a distribution similar to ground truth, creating an incrementally curated training data set that encourages the model to generate improved images over time. After five training iterations, the proposed deep learning-based super-resolution model experienced a 7.5% and 5.49% improvement in structural similarity and peak-signal-to-noise ratio, respectively. Significantly, the model consistently produces visually enhanced results for training, improving its performance while preserving the characteristics of original biomedical images. These findings indicate a potential way to train a deep neural network in a self-revolution manner independent of real-world human data.","PeriodicalId":164997,"journal":{"name":"Conference on Biomedical Photonics and Cross-Fusion","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530716","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}

引用次数: 0