Registration of fluorescein angiography and optical coherence tomography images of curved retina via scanning laser ophthalmoscopy photographs

Almasi, Ramin; Vafaei, Abbas; Ghasemi, Zeinab; Ommani, Mohammad Reza; Dehghani, Amin; Rabbani, Hossein

doi:10.1364/boe.395784

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…Even though the resulting large field intensity projection image looks very similar to the fundus photo we cannot use the Root-Mean-Square (RMS) error between image pixel values to quantify the performance of our algorithm since we are dealing with multimodal images. Instead we chose a method similar to [ 40 ] for grading the accuracy of our registration.…”

Section: Resultsmentioning

confidence: 99%

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

Schwarzhans¹,

Desissaire²,

Steiner³

et al. 2020

Biomed. Opt. Express

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A technique to generate large field of view projection maps of arbitrary optical coherence tomography (OCT) data is described. The technique is divided into two stages - an image acquisition stage that features a simple to use fast and robust retinal tracker to get motion free retinal OCT volume scans - and a stitching stage where OCT data from different retinal locations is first registered against a reference image using a custom pyramid-based approach and finally stitched together into one seamless large field of view (FOV) image. The method is applied to data recorded with a polarization sensitive OCT instrument in healthy subjects and glaucoma patients. The tracking and stitching accuracies are quantified, and finally, large FOV images of retinal nerve fiber layer retardation that contain the arcuate nerve fiber bundles from the optic nerve head to the raphe are demonstrated.

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Section: Resultsmentioning

confidence: 99%

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

Schwarzhans¹,

Desissaire²,

Steiner³

et al. 2020

Biomed. Opt. Express

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“…It is also worth mentioning that, while some of the aforementioned single components in our pipeline are specialized for their respective modalities, the general layout of our pipeline is applicable for registration between sparse 3D cross-section and en face images as long as a spatial alignment from the cross-sectional scans to some en face modality (e.g., IR [ 16 , 17 ], SLO [ 27 , 28 ], reduced OCT volume [ 19 ]) can be established.…”

Section: Technical Discussionmentioning

confidence: 99%

“…Previous work approaching OCT to en face fundus image registration relied on blood vessel segmentation [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Recent work on OCT to en face fluorescein angiography (FAG) registration used scanning laser ophthalmoscopy (SLO) as an intermediate image modality but still required blood vessel segmentation [ 27 , 28 ]. Our proposed pipeline is similar in that we also utilize an intermediate image modality for registration (IR in our case) but different in that we do not require explicit blood vessel segmentation, which might be sensitive to image quality [ 29 ] and structural damages due to progression of CSCR.…”

Section: Introductionmentioning

confidence: 99%

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

et al. 2022

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Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.

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“…MAs and vessels are hard-to-detect objects in OCT images, therefore to prepare the dataset of OCT image strips with appropriate labels, a precise registration is performed to align OCT images and FA photographs. To do so, the dataset and the method proposed in 32 is used for accurate registration of OCT and FA images using SLO photographs as intermediate images. In 32 , dataset includes 36 pairs of FA and SLO images of 21 subjects with diabetic retinopathy, where SLO image pixels are perfectly in correspondence with OCT B-scans.…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning

Almasi

Vafaei

Kazeminasab

et al. 2022

Sci Rep

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Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.

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Registration of fluorescein angiography and optical coherence tomography images of curved retina via scanning laser ophthalmoscopy photographs

Cited by 11 publications

References 33 publications

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning

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