Jeffrey Wigdahl

A Fast and Efficient Technique for the Automatic Tracing of Corneal Nerves in Confocal Microscopy

Purpose We describe a novel fully automatic method capable of tracing the subbasal plexus nerves from human corneal confocal images. Methods Following an increasing interest in the automatic analysis of corneal nerves, a few approaches have been proposed. These, however, cannot cope with large images, such as mosaics, in due time. The rationale of the proposed method is to minimize required computing time while still providing accurate results. Our method consists of two sequential steps – a thresholding step followed by a supervised classification. For the classification we use a support vector machines (SVM) approach. Initially, a large set of features is computed, which is later reduced using a backward-elimination based on segmentation accuracy. To validate the obtained tracings, we evaluated the tracing accuracy and reliability of extracted clinical parameters (corneal nerves density and tortuosity). Results The proposed algorithm proved capable to correctly trace 0.89 ± 0.07 of the corneal nerves. The obtained performance level was comparable to a second human grader. Furthermore, the proposed approach compares favorably to other methods. For both evaluated clinical parameters the proposed approach performed well. An execution time of 0.61 ± 0.07 seconds per image was achieved. The proposed algorithm was applied successfully to mosaic images, with run times of the order of tens of seconds. Conclusions The achieved quality and processing time of the proposed method appear adequate for the application of this technique to clinical practice. Translational Relevance The automatic tracing of corneal nerves is an important step for the quantitative analysis of corneal nerves in daily clinical practice. The proposed fast technique allows features, such as corneal nerve density and tortuosity, to be computed in a few seconds. The application of nerve tracing to mosaics covering a large area can be a key component in clinical studies aimed at investigating neuropathy influence in various ocular or systemic diseases.

Automatic montaging of corneal sub-basal nerve images for the composition of a wide-range mosaic

We present and discuss a computerized system able to provide a wide-range mosaic of the sub-basal nerve layer of central cornea, built from several images acquired in-vivo with confocal microscopy. The montage is performed by a fast, reliable and fully automatic computerized system that does not require any expedient or manual adjustment during the acquisition process. The resulting mosaic provides a large high quality image, which should significantly aid clinicians in evaluating and assessing in a more reliable way the pathologic signs of interest.

Automatic Gunn and Salus sign quantification in retinal images

Prolonged hypertension can lead to abnormal changes in the retinal vasculature, including sclerosis and thickening of the arteriole walls. These changes can cause compression (Gunns sign) and deflection (Saluss sign) of the veins at arteriovenous crossings. In retinal images, Gunns sign appears as a tapering of the vein at a crossing point, while Saluss sign presents as an S-shaped curving. This paper presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. The method was tested on a small set of crossings, graded by a set of 3 doctors who were in agreement as having or not having Gunn/Salus sign. Results show separation between the two classes and that we can reliably detect and quantify these sign under the right conditions.

A fully-automatic fast segmentation of the sub-basal layer nerves in corneal images

Corneal nerves changes have been linked to damage caused by surgical interventions or prolonged contact lens wear. Furthermore nerve tortuosity has been shown to correlate with the severity of diabetic neuropathy. For these reasons there has been an increasing interest on the analysis of these structures. In this work we propose a novel, robust, and fast fully automatic algorithm capable of tracing the sub-basal plexus nerves from human corneal confocal images. We resort to logGabor filters and support vector machines to trace the corneal nerves. The proposed algorithm traced most of the corneal nerves correctly (sensitivity of 0.88 ± 0.06 and false discovery rate of 0.08 ± 0.06). The displayed performance is comparable to a human grader. We believe that the achieved processing time (0.661 ± 0.07 s) and tracing quality are major advantages for the daily clinical practice.

Measuring blood flow velocity from intravital video recordings.

There is an obvious scientific interest in computing blood flow velocity from intravital microscopy using digital video cameras attached to microscopes. Therefore, software capable of measuring blood flow velocity from videos is of major importance. In this work, a novel software tool is presented. The software tackles three main issues in velocity measurement from videos, the registration, segmentation, and finally the measuring itself. The software was tested in chick chorioallantoic membrane (CAM) videos captured with different resolutions, frame rates, and even cameras. The obtained results show the robustness achieved.

Automatic detection of microdots in the stromal layer of corneal images

Microdots are bright, 1-2um features of the cornea. It has not been proven what these dots represent, but they are thought to be remnants of apoptotic cell death, such as lipofuscin granules. Their presence has been shown to correlate with corneal aging and extended contact use, both of which are linked to oxygen deprivation in the cornea. Confocal images of the stroma show these microdots mixed with larger keratocyte cells. This paper presents a method for detecting microdots using a two-step filtering scheme that separates the keratocyte cells and the microdots. Keratocyte cell locations are then used to eliminate falsely detected microdots. Results are compared to ground truth based on a grading scale from 0-5. Two graders were given a set of 50 images to grade using a GUI that included sample images for each of the six grades. The two graders had a correlation of .88 with each other. The algorithm had a correlation of .88 with the average of graders and .85 with each of the graders individually.