Novel segmentation algorithm for high-throughput \nanalysis of spectral domain-optical coherence \ntomography imaging of teleost retina
Notice bibliographique
Résumé
Aim Spectral Domain-Optical Coherence Tomography (SD-OCT) has become an \nessential tool to assess the health of ocular tissues in live subjects. The processing \nof SD-OCT images, in particular from non-mammalian species, is a labour-intensive \nmanual process due to the lack of access to analytical programs. The work presented \nherein describes the development and implementation of a novel computer algorithm \nfor quantitative analysis of SD-OCT images of live teleost eyes. We hypothesized that \nthis algorithm, in comparison to manual segmentation of SD-OCT images, will allow \nmore precise measurement, with significantly higher throughput capacity, of retinal \narchitecture in live teleost ocular tissue. \nMethods Automated segmentation processing of SD-OCT images of the retinal \nlayers was developed using a novel algorithm based on thresholding, which operates \non the pixel values contained in an image. The algorithm measured the thickness \ncharacteristic of the retina present in the input dataset to provide increased accuracy \nand repeatability of SD-OCT analysis over manual measurements. The program was \nalso designed to allow adjustments of the thresholding variables to suit any specific \nimage set. A heat map software was created alongside the algorithm to plot the \nSD-OCT image measurements as a colour gradient. \nResults Automated segmentation analysis of the retinal layers from SD-OCT images \nenabled analysis of a large volume of imaging data of teleost ocular structures \nin a short time. The algorithm was just as accurate when compared to manual measurements \nand provided repeatability as measurements could be quickly reassessed to \nconfirm previously determined results. This is the case as the algorithm can generate \nhundreds of retinal thickness measurements per image for a large number of images \nfor a given dataset. This algorithm can be deemed as repeatable as each input will \nalways produce the same output due to the thresholding methods used. This is the \ncase as thresholding is a finite mathematical process to determine a range of values. \nThe measurements produced from this assessment were represented by a heat map \nsoftware that directly converted the measurements taken from each processed image \nto represent the changes in thickness across the whole retinal scan. \nConclusions Our work addresses the need for accurate and high-throughput SDOCT \ndata analysis for the retinal tissues of teleosts where previously no such program \nexisted. Our heat mapping software enables the visualization of the retinal thickness \nacross the whole retinal scans facilitating the comparison of specimens and localization \nof areas of interest. Our novel algorithm provides the first tools to analyze SD-OCT \nscans of non-mammalian species at a faster rate than manual analysis, increasing the \npotential of future research output. The adaptability of our programs makes them \npotentially suitable for the analysis of SD-OCT scans from other non-mammalian \nspecies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».