Segmentation of cyctoids macular edema in optical cohenrence tomography

Trabelsi, H.; Zghal, I.; Slokom, Nesrine

doi:10.1109/atsip.2016.7523096

Cited by 6 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This step is important to provide satisfactory results in segmentation. Various preprocessing techniques can be used, such as: brightness and contrast adjustment, noise reduction, correction of irregular lighting, edge enhancement, among others [43], [44].…”

Section: A Image Preprocessingmentioning

confidence: 99%

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images

Mendes

Lucena

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The retina is a part of the ocular system responsible for vision. In the central region of the retina is the macula, that enables detailed view. There is a distinct macular disease called Macular Hole (MH). It causes a condition of low vision related to the weakening of the fovea, high myopia, eye trauma and severe exposure to the sun. A surgery depends of the size and shape of the MH. A macular hole can be identified in Optical Coherence Tomography (OCT) images through the top boundaries of the Internal Limiting Membrane (ILM) and the Retinal Pigment Epithelium (RPE). Manual segmentation of OCT images is time consuming whereas automatic segmentation is fast and has a low computational cost, and consequently of interest to specialists. Thus, the main objective of this work is to develop an algorithm that automatically segments the ILM boundary layer and the area of the MH in OCT images. Another objective that was also pursued included the automatic acquisition of MH measurements. The segmentation was performed through a set of techniques involving shortest distance from a point to a curve (Euclidean Distance), Flood Fill and Border Following algorithms. The proposed method reached satisfactory results for all applications made. The automatic segmentation of MH and the extraction of its measures is a significant contribution to aid the medical diagnosis of the macular hole pathology.

show abstract

Section: A Image Preprocessingmentioning

confidence: 99%

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images

Mendes

Lucena

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Correia et al [37] used Monte Carlo simulation to find the changes in the OCT images of DME patients from the cellular perspective. Trabelsi et al [38] presented a technique to detect cystoids macular edema from OCT images.…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection of Non-Proliferative Diabetic Retinopathy in Optical Coherence Tomography Images Using Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a disease that forms as a complication of diabetes. It is particularly dangerous since it often goes unnoticed and can lead to blindness if not detected early. Despite the clear importance and urgency of such an illness, there is no precise system for the early detection of DR so far. Fortunately, such system could be achieved using deep learning including convolutional neural networks (CNNs), which gained momentum in the field of medical imaging due to its capability of being effectively integrated into various systems in a manner that significantly improves the performance. This paper proposes a computer aided diagnostic (CAD) system for the early detection of non-proliferative DR (NPDR) using CNNs. The proposed system is developed for the optical coherence tomography (OCT) imaging modality. Throughout this paper, all aspects of deployment of the proposed system are studied starting from the preprocessing stage required to extract input retina patches to train the CNN without resizing the image, to the use of transfer learning principals and how to effectively combine features in order to optimize performance. This is done through investigating several scenarios for the system setup and then selecting the best one, which from the results revealed to be a two pre-trained CNNs based system, in which one of these CNNs is independently fed by nasal retina patches and the other one by temporal retina patches. The proposed transfer learning based CAD system achieves a promising accuracy of 94%. INDEX TERMS Convolutional neural network (CNN), diabetic retinopathy (DR), optical coherence tomography (OCT).

show abstract

“…Zhang et al 19 calculated the volume of CME with macular hole (MH) in 3D OCT scans. Slokom et al 31 proposed a CAD system that detects cystoids to distinguish CME regions in OCT scans of the macula. For AMD, Oliveira et al 32 developed a system that combined sparse higher order potentials into a multisurface segmentation framework.…”

Section: Introductionmentioning

confidence: 99%