![]() ![]() Besides, AdipoCount software lacks threshold settings and one-step area measurement function, thus the counting mistake could not be adjusted, and final results have to be calculated. During our operation, we found that adipocytes with empty holes, cell debris, and cells adhesion edges may also be mistakenly counted by AdipoCount software. Recently, a freely available counting software for adipocyte numbers, AdipoCount, is reported to be convenient and has powerful capability to recognize adipocyte boundaries and segment images with simple operations for further analysis ( 19). However, these automatic methods may require programming knowledge or plug-ins, and the counting results also rely on slide quality, software recognition, so the accuracy is often unsatisfactory ( 15– 18). Researchers have been committed to developing software that can identify adipocytes accurately and efficiently ( 13, 14). Meanwhile, drawing the adipocyte boundaries manually by IPP and ImageJ is accurate but time-consuming and intractable to large-scale investigations. However, because adipocytes are membrane-stained, and clear contrast or threshold is difficult to be set by these tools, the automatic counting of adipocyte sizes on adipose tissue slides may result in severe measurement errors. Area of interest (AOI) and measurement threshold of adipose tissue slides can be set to screen out objects that meet the requirements automatically ( 11, 12). ImageJ and Image-Pro Plus (IPP) are both the open-software platform image processing tools for scientific image analysis and have been widely used in life sciences ( 9, 10). Therefore, quantification of the number and size of adipocytes is essential for assessing the severity of obesity and providing the reference for the choice of treatment ( 5).Īs technology advances, image processing software have been developed for the measurement of adipocytes ( 6– 8). When the capacity of adipocytes cannot meet the demand for lipid storage, excess lipids will be ectopically deposited in the muscles, liver, and pancreas, causing metabolic abnormalities, such as insulin resistance ( 3, 4). Obesity results from energy intake in excess of energy expenditure, and surplus energy is stored in adipocytes as a form of triglycerides, which results in adipocyte hyperplasia and hypertrophy ( 1, 2). Overall, we developed a combination method to measure adipocyte sizes accurately and efficiently, which may be helpful for experimental process in laboratory and also for clinic diagnosis. Besides, the recognition effect of monochrome segmentation image is better than that of color segmentation image. We found that the adipocyte sizes quantified by the tool combination were comparable as manual measurement, whereas the combined methods were more efficient. In the present study, we introduced the protocol of combined usage of these tools and illustrated the process with histological staining slides from adipose tissues of lean and obese mice. Although commercially available adipocyte counting tools, including AdipoCount, Image-Pro Plus, and ImageJ were helpful, limitations still exist in accuracy and time consuming. Manual measurement is accurate but time-consuming. After histological staining, it is critical to accurately measure the number and size of adipocytes for assessing the severity of obesity in a timely fashion. One of the major characteristics of obesity is fat accumulation, including hyperplasia (increase in number) and hypertrophy (increase in size). In recent decades, the prevalence of obesity has been rising. 3Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.2Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China.1Department of Endocrine and Metabolic Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.Yepeng Hu 1† Jian Yu 2† Xiangdi Cui 2 Zhe Zhang 2 Qianqian Li 1 Wenxiu Guo 2 Cheng Zhao 2 Xin Chen 2 Meiyao Meng 2 Yu Li 2 Mingwei Guo 2 Jin Qiu 2 Fei Shen 3 Dongmei Wang 2 Xinran Ma 1,2 Lingyan Xu 1,2* Feixia Shen 1* Xuejiang Gu 1* ![]()
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