Computer-Assisted Histopathological Calculation Analysis of the Sciatic Nerve of Diabetic Neuropathy Rat Model

• Main Authors: Indah Tri Lestari (Author), Kusnandar Anggadiredja (Author), Afrillia Nuryanti Garmana (Author), Sevi Nurafni (Author)
• Format: Book
• Published: Institute for Researches and Community Services Universitas Muhammadiyah Palangkaraya, 2024-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.33084/bjop.v7i2.6590; 2621-4814
• Keywords: Histopathology digitization; Gaussian adaptive threshold; ImageJ plugin; Sciatic nerve; Pharmacy and materia medica; RS1-441
• Source: Borneo Journal of Pharmacy, Vol 7, Iss 2 (2024)

Histopathology is the science that studies the signs of disease by studying the structural and functional changes that occur in cells using certain types of dyes such as hematoxylin and eosin (H&E). Traditionally histopathological testing is carried out using semi-quantitative methods. A more advanced method is done by taking photos digitally, and then digital photos are quantified with the help of software such as ImageJ using plug-in tools. Recent advances in digital pathology require the development of more efficient computerized image analysis such as the Gaussian adaptive threshold method. This research aims to compare the calculation results of computer-assisted digitalization of histopathology using the ImageJ plugin manual method with automatic calculations using Gaussian adaptive threshold to quantify the amount of sciatic nerve cell damage in the Diabetic peripheral neuropathy (DPN) rat model. In this study, two image analysis methods were used to test their ability to measure the amount of cell damage in the sciatic nerve of normal rats using a model of diabetic neuropathy. The first method uses the ImageJ plugin manual. The second method is the Gaussian adaptive threshold method. The ImageJ plugin manual method obtained a cell abnormality value of 213 cells. Meanwhile, with the Gaussian adaptive threshold method, a value of 204 cells was obtained. The calculation results of the two methods show an insignificant difference between the methods p >0.05. This study presents a computerized morphometric image analysis method with the potential for pathology digitalization applications.