ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images
| Autor: | Shoji Notomi, Daniel E. Maidana, Demetrios G. Vavvas, Josep Maria Caminal-Mitjana, Danica Joseph, Tianna Zhou, Takashi Ueta, Cassandra Kosmidou, Joan W. Miller |
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| Rok vydání: | 2020 |
| Předmět: |
Cell death
0301 basic medicine Coefficient of variation lcsh:Medicine Image processing Article Retina Mice 03 medical and health sciences chemistry.chemical_compound Digital image 0302 clinical medicine Image Processing Computer-Assisted medicine Calibration Animals lcsh:Science Outer nuclear layer Mathematics Multidisciplinary lcsh:R Computational science Retinal Detachment Retinal 030206 dentistry Retina Layer Disease Models Animal 030104 developmental biology medicine.anatomical_structure chemistry Imatges mèdiques lcsh:Q Tomography Software Tomography Optical Coherence Imaging systems in medicine Biomedical engineering |
| Zdroj: | Dipòsit Digital de la UB Universidad de Barcelona Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) |
| ISSN: | 2045-2322 |
| Popis: | To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. In the calibration dataset, there were no differences in layer thickness between measured and known thickness masks, with an overall coefficient of variation of 0.00%. Training dataset measurements of immunofluorescence retina nuclear layers disclosed no significant differences between TT and any observer’s average outer nuclear layer (ONL) (p = 0.998), inner nuclear layer (INL) (p = 0.807), and ONL/INL ratio (p = 0.944) measurements. Agreement analysis showed that bias between TT vs. observers’ mean was lower than between any observers’ mean against each other in the ONL (0.77 ± 0.34 µm vs 3.25 ± 0.33 µm) and INL (1.59 ± 0.28 µm vs 2.82 ± 0.36 µm). Validation dataset showed that TT can detect significant and true ONL thinning (p = 0.006), more sensitive than manual measurement capabilities (p = 0.069). ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. In addition, the TT can be customized to user preferences and is freely available to download. |
| Databáze: | OpenAIRE |
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