Corneal Endothelial Cell Segmentation by Classifier-driven Merging of Oversegmented Images
| Autor: | Juan P. Vigueras-Guillén, Eleni-Rosalina Andrinopoulou, Hans G Lemij, Angela Engel, Lucas J. van Vliet, Koenraad A. Vermeer, Jeroen van Rooij |
|---|---|
| Přispěvatelé: | Epidemiology |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Corneal endothelium
Watershed Support Vector Machine Databases Factual Computer science Swine Cell segmentation Image processing confocal microscopy Optical microscopy Merging law.invention Cornea 03 medical and health sciences 0302 clinical medicine Confocal microscopy law Microscopy In vivo medicine Image Processing Computer-Assisted Animals Segmentation Electrical and Electronic Engineering Stochastic Processes Image segmentation Retinal pigment epithelium Microscopy Confocal Support vector machines Radiological and Ultrasound Technology business.industry Endothelium Corneal Pattern recognition merging superpixels Fluorescence Computer Science Applications medicine.anatomical_structure Specular microscopy 030221 ophthalmology & optometry Artificial intelligence business stochastic watershed 030217 neurology & neurosurgery Software |
| Zdroj: | IEEE Transactions on Medical Imaging, 37(10) IEEE Transactions on Medical Imaging, 37(10), 2278-2289. Institute of Electrical and Electronics Engineers Inc. |
| ISSN: | 0278-0062 |
| Popis: | Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor’s built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types ( ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets’ authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precision were obtained. |
| Databáze: | OpenAIRE |
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