11/28/2023 0 Comments Multi measure plugin imagej![]() ![]() Digital image dataset Calibration dataset C57BL/6J mice were purchased from The Jackson Laboratories and maintained in a standard 12-h light/dark cycle. To validate the ThicknessTool in experimental model, a retinal detachment was induced in eight-week-old C57BL/6J mice, as previously described 10. Animal protocols were reviewed and approved by the Animal Care Committee of the Massachusetts Eye and Ear Infirmary. In addition, TT can process images from multiple imaging modalities.Īll animals used in experiments and breeding adhered to the statement of the Association for Research in Vision and Ophthalmology (ARVO). We found that this measurement tool can provide accurate and precise thickness measurements. For this purpose, we developed the ThicknessTool (TT) and validated its accuracy by objective calibration and agreement analysis with two masked observers. The purpose of this work was to develop an automated retinal layer thickness measurement tool for the ImageJ platform, which can quantitate nuclear layers following the retina contour, with callipers as close to 1-pixel to each other. Despite pioneering work done in this area 9, to the best of our knowledge, there is currently no freely available tool for the ImageJ platform to automatedly quantitate multiple layer thicknesses in large images and in different imaging modalities. Therefore, a tool that can adapt to layer architecture and contour to measure thickness in a broad segment, in multiple layers, in either single images or large tiles, and in multiple platforms, is very compelling. As new imaging modalities also become available, a versatile method that can adapt to multiple imaging modalities is ideal. Furthermore, given the high magnification of microscope images, a small area of interest is often analysed to expedite the analysis, which can lead to bias. In addition, given the curvature of the retina, manually positioning callipers perpendicular to the layer contour along the long axis can be difficult. However, the advantage of this approach can be compromised as callipers are manually drawn and measured by an observer. The utility of retina ONL thickness quantitation to infer photoreceptor degeneration relies on manual calliper measurements. Given the technical and time constraints related to cell death assays and manual counting to date, outer nuclear layer (ONL) thickness has been widely used as a practical proxy method to estimate the depth of photoreceptor cell death 6, 7, 8. Among these methods, photoreceptor cell death assays 3, 4, outer nuclear layer cell counting 5, and outer nuclear layer thickness 6, 7, have been used in animal models to quantitate photoreceptor degeneration. Inasmuch, quantitation of this cell loss has been addressed by a myriad of approaches, predominantly in experimental models of retinal diseases, such as retinal detachment 1, 2. Progressive photoreceptor cell death is a significant culprit in retinal degenerative diseases. In addition, the TT can be customized to user preferences and is freely available to download. ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. Validation dataset showed that TT can detect significant and true ONL thinning ( p = 0.006), more sensitive than manual measurement capabilities ( p = 0.069). Agreement analysis showed that bias between TT vs. 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. 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%. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. ![]() Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. ![]() To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. ![]()
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