Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning

IF 2.1 Q3 ENDOCRINOLOGY & METABOLISM
Liping Liu , Bozhi Cai , Lingling Liu , Xiaoning Zhuang , Zhidan Zhao , Xin Huang , Jianfa Zhang
{"title":"Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning","authors":"Liping Liu ,&nbsp;Bozhi Cai ,&nbsp;Lingling Liu ,&nbsp;Xiaoning Zhuang ,&nbsp;Zhidan Zhao ,&nbsp;Xin Huang ,&nbsp;Jianfa Zhang","doi":"10.1016/j.bonr.2024.101743","DOIUrl":null,"url":null,"abstract":"<div><p>The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that:</p><ul><li><span>1)</span><span><p>SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(<em>P</em> &lt; 0.01).</p></span></li><li><span>2)</span><span><p>In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (<em>P</em> &lt; 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed.</p></span></li><li><span>3)</span><span><p>Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively.</p></span></li></ul><p>In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice.</p></div>","PeriodicalId":9043,"journal":{"name":"Bone Reports","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235218722400010X/pdfft?md5=6b1d414a042c7c8d19bfb788d60f2835&pid=1-s2.0-S235218722400010X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bone Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S235218722400010X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

Abstract

The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that:

  • 1)

    SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(P < 0.01).

  • 2)

    In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (P < 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed.

  • 3)

    Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively.

In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice.

基于同步辐射相位对比成像计算机断层扫描和深度学习的糖尿病小鼠部分骨折愈合过程形态结构研究
近年来,糖尿病的发病率明显上升,从而增加了骨折的易感性并阻碍了骨折愈合的过程。本研究的主要目的是采用同步辐射相位对比成像计算机断层扫描(SR-PCI-CT)技术,研究糖尿病部分骨折小鼠模型中不同类型胼胝体的形态和结构属性。此外,还利用深度学习图像分割模型对不同时间间隔内的胼胝体进行了定性和定量分析。将年龄为五周的四十只雄性昆明小鼠随机分为两组,每组二十只,即单纯骨折组(SF)和糖尿病骨折组(DF)。DF组小鼠连续5天腹腔注射60 mg/kg 1 %链脲佐菌素(STZ)溶液,以最后一次注射STZ一周后空腹血糖水平≥11.1 mmol /l为建模标准。观察发现,所有小鼠的右胫骨都有斜形骨折,且骨折没有横穿整个骨骼。在骨折发生后的三日、七日、十日和十四日,提取骨折的胫骨进行 SR-PCI-CT 成像和组织学分析。此外,还设计了一个深度学习图像分割模型,用于自动检测、分类和定量检查不同类型的胼胝体。利用图像 J 软件测量不同类型胼胝体的灰度值并进行定量分析。研究结果表明:1)SR-PCI-CT 成像有效地描述了骨折愈合过程中不同类型胼胝体的形态属性。2)与 SF 组相比,DF 组同期的胼胝体总量显著减少(P< 0.01)。3)组织学为深度学习图像分割模型的训练算法提供了基础。在测试集中,深度学习图像分割模型对后备/增生软骨、肥大软骨和矿化软骨的准确度分别为 0.69、0.81 和 0.733。综上所述,SR-PCI-CT 图像接近于组织学水平,与组织学检查相比,同步辐射 CT 图像可识别多种软骨,而人工智能图像分割模型可通过深度学习实现自动分析和数据生成,进一步确定 SR-PCI-CT 识别各种软骨组织的客观性和准确性。因此,该成像技术结合深度学习图像分割模型可有效评估糖尿病对小鼠骨折愈合过程中胼胝体形态和结构变化的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Bone Reports
Bone Reports Medicine-Orthopedics and Sports Medicine
CiteScore
4.30
自引率
4.00%
发文量
444
审稿时长
57 days
期刊介绍: Bone Reports is an interdisciplinary forum for the rapid publication of Original Research Articles and Case Reports across basic, translational and clinical aspects of bone and mineral metabolism. The journal publishes papers that are scientifically sound, with the peer review process focused principally on verifying sound methodologies, and correct data analysis and interpretation. We welcome studies either replicating or failing to replicate a previous study, and null findings. We fulfil a critical and current need to enhance research by publishing reproducibility studies and null findings.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信