Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks
المؤلفون المشاركون
Yang, Wei
Lu, Yanmeng
Cao, Lei
Li, ChuangQuan
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-25
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Pathological classification through transmission electron microscopy (TEM) is essential for the diagnosis of certain nephropathy, and the changes of thickness in glomerular basement membrane (GBM) and presence of immune complex deposits in GBM are often used as diagnostic criteria.
The automatic segmentation of the GBM on TEM images by computerized technology can provide clinicians with clear information about glomerular ultrastructural lesions.
The GBM region on the TEM image is not only complicated and changeable in shape but also has a low contrast and wide distribution of grayscale.
Consequently, extracting image features and obtaining excellent segmentation results are difficult.
To address this problem, we introduce a random forest- (RF-) based machine learning method, namely, RF stacks (RFS), to realize automatic segmentation.
Specifically, this work proposes a two-level integrated RFS that is more complicated than a one-level integrated RF to improve accuracy and generalization performance.
The integrated strategies include training integration and testing integration.
Training integration can derive a full-view RFS1 by simultaneously sampling several images of different grayscale ranges in the train phase.
Testing integration can derive a zoom-view RFS2 by separately sampling the images of different grayscale ranges and integrating the results in the test phase.
Experimental results illustrate that the proposed RFS can be used to automatically segment different morphologies and gray-level basement membranes.
Future study on GBM thickness measurement and deposit identification will be based on this work.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Cao, Lei& Lu, Yanmeng& Li, ChuangQuan& Yang, Wei. 2019. Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130471
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Cao, Lei…[et al.]. Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1130471
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Cao, Lei& Lu, Yanmeng& Li, ChuangQuan& Yang, Wei. Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130471
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130471
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر