Matching 3D OCT retina images into super-resolution dataset

Stankiewicz, Agnieszka; Marciniak, Tomasz; Dąbrowski, Adam; Stopa, Marcin; Marciniak, Elzbieta; Michalski, Andrzej

doi:10.1109/spa.2016.7763600

Cited by 8 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 68 , 69 Köhler and colleagues 70 demonstrated how a multiframe super-resolution framework can be used to reconstruct a single high-resolution image from sequential low-resolution video frames. Stankiewicz and colleagues 71 implemented a similar framework for reconstructing super-resolved volumetric OCT stacks from several low-quality volumetric OCT scans. Neither of these approaches, however, applied the reconstruction in real time.…”

Section: Embedded Ophthalmic Devicesmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

View full text Add to dashboard Cite

Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for ‘active acquisition’–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via ‘active acquisition’ serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining.

show abstract

Section: Embedded Ophthalmic Devicesmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

View full text Add to dashboard Cite

show abstract

“…This procedure is repeated to generate an array of adjacent B-scans that constitutes the 3D OCT volume. 13 The reflectivity and morphology of retinal layers can be quantified by extracting a single layer from the volume i.e., layer segmentation. One option for layer segmentation is to use available software for image editing to outline retinal layers manually.…”

Section: Introductionmentioning

confidence: 99%

“…An A‐scan juxtaposition is taken along a line on the surface to provide a cross‐sectional image (B‐scan). This procedure is repeated to generate an array of adjacent B‐scans that constitutes the 3D OCT volume 13 . The reflectivity and morphology of retinal layers can be quantified by extracting a single layer from the volume i.e., layer segmentation.…”

Section: Introductionmentioning

confidence: 99%

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

Sleman¹,

Soliman²,

Elsharkawy³

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data. Methods: To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula midarea) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented. Results: This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches. Conclusions: The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.

show abstract

“…However, an unquestionable advantage of this technology, in addition to the noninvasive examination, is the ability to precisely combine the image of the segmented vessels with the image of the retinal layers. It enables to prepare a so-called virtual fundus map for a physician planning an operation [8].…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Marciniak

Stankiewicz²,

Dąbrowski

et al. 2019

View full text Add to dashboard Cite

In this paper methods and their examination results for automatic segmentation and parameterization of vessels based on spectral domain optical coherence tomography (SD-OCT) of the retina are presented. We present three strategies for morphologic image processing of a fundus image reconstructed from OCT scans. A specificity of initial image processing for fundus reconstruction is analysed. Then, the parameterization step is performed based on the vessels segmented with the proposed algorithm. The influence of various methods on the vessel segmentation and fully automatic vessel measurement is analysed. Experiments were carried out with a set of 3D OCT scans obtained from 24 eyes (12 healthy volunteers) with the use of an Avanti RTvue OCT device. The results of automatic vessel segmentation were numerically compared with those prepared manually by the medical doctor experts.

show abstract

Matching 3D OCT retina images into super-resolution dataset

Cited by 8 publications

References 24 publications

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

Metrology and Measurement Systems

Contact Info

Product

Resources

About