RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network.

Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD.

Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy.

Purpose: To design a robust and automated hyperreflective foci (HRF) segmentation framework for spectral-domain optical coherence tomography (SD-OCT) volumes, especially volumes with low HRF-background contrast. Methods: HRF in retinal SD-OCT volumes appear with low-contrast characteristics that results in the difficulty of HRF segmentation. Therefore to effectively segment the HRF we proposed a fully automated method for HRF segmentation in SD-OCT volumes with diabetic retinopathy (DR). First, we generated the enhanced SD-OCT images from the denoised SD-OCT images with an enhancement method. Then the enhanced images were cascaded with the denoised images as the two-channel input to the network against the low-contrast HRF. Finally, we replaced the standard convolution with slice-wise dilated convolution in the last layer of the encoder path of 3D U-Net to obtain long-range information. Results: We evaluated our method using two-fold cross-validation on 33 SD-OCT volumes from 27 patients. The average dice similarity coefficient was 70.73%, which was higher than that of the existing methods with significant difference (P < 0.01). Conclusions: Experimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance. Translational Relevance: Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR.

Hyperreflective Foci Enhancement in a Combined Spatial-Transform Domain for SD-OCT Images.

Purpose: Spectral-domain optical coherent tomography (SD-OCT) is a useful tool for visualizing, treating, and monitoring retinal abnormality in patients with different retinal diseases. However, the assessment of SD-OCT images is thwarted by the lack of image quality necessary for ophthalmologists to analyze and quantify the diseases. This has hindered the potential role of hyperreflective foci (HRF) as a prognostic indicator of visual outcome in patients with retinal diseases. We present a new multi-vendor algorithm that is robust to noise while enhancing the HRF in SD-OCT images. Methods: The proposed algorithm processes the SD-OCT images in two parallel processes simultaneously. The two parallel processes are combined by histogram matching. An inverse of both logarithmic and orthogonal transforms is applied to the mapped data to produce the enhanced image. Results: We evaluated our algorithm on a dataset composed of 40 SD-OCT volumes. The proposed method obtained high values for the measure of enhancement, peak signal-to-noise ratio, structure similarity, and correlation (ρ) and a low value for mean square error of 36.72, 38.87, 0.87, 0.98, and 25.12 for Cirrus; 40.77, 41.84, 0.89, 0.98, and 22.15 for Spectralis; and 30.81, 32.10, 0.81, 0.96, and 28.55 for Topcon SD-OCT devices, respectively. Conclusions: The proposed algorithm can be used in the medical field to assist ophthalmologists and in the preprocessing of medical images. Translational Relevance: The proposed enhancement algorithm facilitates the visualization and detection of HRF, which is a step forward in assisting clinicians with decision making about patient treatment planning and disease monitoring.

Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy.

The presence of hyperreflective foci (HFs) is related to retinal disease progression, and the quantity has proven to be a prognostic factor of visual and anatomical outcome in various retinal diseases. However, lack of efficient quantitative tools for evaluating the HFs has deprived ophthalmologist of assessing the volume of HFs. For this reason, we propose an automated quantification algorithm to segment and quantify HFs in spectral domain optical coherence tomography (SD-OCT). The proposed algorithm consists of two parallel processes namely: region of interest (ROI) generation and HFs estimation. To generate the ROI, we use morphological reconstruction to obtain the reconstructed image and histogram constructed for data distributions and clustering. In parallel, we estimate the HFs by extracting the extremal regions from the connected regions obtained from a component tree. Finally, both the ROI and the HFs estimation process are merged to obtain the segmented HFs. The proposed algorithm was tested on 40 3D SD-OCT volumes from 40 patients diagnosed with non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and diabetic macular edema (DME). The average dice similarity coefficient (DSC) and correlation coefficient (r) are 69.70%, 0.99 for NPDR, 70.31%, 0.99 for PDR, and 71.30%, 0.99 for DME, respectively. The proposed algorithm can provide ophthalmologist with good HFs quantitative information, such as volume, size, and location of the HFs.

Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy.

We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient ([Formula: see text]) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.