Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoomin-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. 2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201

Geodesic Graph Cut Based Retinal Fluid Segmentation in Optical Coherence Tomography

Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. Its most damaging form is characterized by accumulation of fluid inside the retina, whose quantification is of utmost importance for evaluating the disease progression. In this paper we propose an automated method for retinal fluid segmentation from 3D images acquired with optical coherence tomography (OCT). It combines a machine learning approach with an effective segmentation framework based on geodesic graph cut. After an image preprocessing step, an artificial neural network is trained based on textural features to assign to each voxel a probability of belonging to a fluid. The obtained probability maps are used to compute minimal geodesic distances from a set of identified seed points to the remaining unassigned voxels. Finally, the segmentation is solved optimally and efficiently using graph cut optimization. The method is evaluated on a clinical longitudinal dataset consisting of 30 OCT scans from 10 patients taken at 3 different stages of treatment. Manual annotations from two retinal specialists were taken as the gold standard. The segmentation method achieved mean precision of 0.88 and recall of 0.83, with the combined F1 score of 0.85. The segmented fluid volumes were within the measured inter-observer variability. The results demonstrate that the proposed method is a promising step towards accurate quantification of retinal fluid

Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina. Detecting diabetic retinopathy requires physicians to look at the whole image and fixate on some specific regions to locate potential biomarkers of the disease. Therefore, getting inspiration from ophthalmologist, we propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions. To evaluate the performance of this proposed ensemble, we used publicly available EyePACS and Messidor datasets. Extensive experimentation for binary, ternary and quaternary classification shows that this ensemble largely outperforms individual image classifiers as well as most of the published works in most training setups for diabetic retinopathy detection. Furthermore, the performance of fine-grained classifiers is found notably superior than coarse-grained image classifiers encouraging the development of task-oriented fine-grained classifiers modelled after specialist ophthalmologists.Comment: Pages 12, Figures

Blood particulate analogue fluids: A review

Microfluidics has proven to be an extraordinary working platform to mimic and study blood flow phenomena and the dynamics of components of the human microcirculatory system. However, the use of real blood increases the complexity to perform these kinds of in vitro blood experiments due to diverse problems such as coagulation, sample storage, and handling problems. For this reason, interest in the development of fluids with rheological properties similar to those of real blood has grown over the last years. The inclusion of microparticles in blood analogue fluids is essential to reproduce multiphase effects taking place in a microcirculatory system, such as the cell-free layer (CFL) and Fähraeus–Lindqvist effect. In this review, we summarize the progress made in the last twenty years. Size, shape, mechanical properties, and even biological functionalities of microparticles produced/used to mimic red blood cells (RBCs) are critically exposed and analyzed. The methods developed to fabricate these RBC templates are also shown. The dynamic flow/rheology of blood particulate analogue fluids proposed in the literature (with different particle concentrations, in most of the cases, relatively low) is shown and discussed in-depth. Although there have been many advances, the development of a reliable blood particulate analogue fluid, with around 45% by volume of microparticles, continues to be a big challengeThis research was funded by the Spanish Ministry of Science and Education Grant No. PID2019-108278RB-C32 / AEI / 10.13039/501100011033, and Junta de Extremadura (Spain) Grant Nos. GR18175 and IB18005 (partially financed by FEDER funds). The authors also acknowledge the Fundação para a Ciência e a Tecnologia (FCT) for partially financing the research under the strategic grants UIDB/04077/2020, UIDB/00532/2020, and the project NORTE-01-0145-FEDER030171 (PTDC/EME-SIS/30171/2017) funded by COMPETE2020, NORTE 2020, PORTUGAL 2020, Lisb@2020, and FEDE

Segmentation of the Retinal Vasculature within Spectral-Domain Optical Coherence Tomography Volumes of Mice

Automated approaches for the segmentation of the retinal vessels are helpful for longitudinal studies of mice using spectral-domain optical coherence tomography (SD-OCT). In the SD-OCT volumes of human eyes, the retinal vasculature can be readily visualized by creating a projected average intensity image in the depth direction. The created projection images can then be segmented using standard approaches. However, in the SD-OCT volumes of mouse eyes, the creation of projection images from the entire volume typically results in very poor images of the vasculature. The purpose of this work is to present and evaluate three machine-learning approaches, namely baseline, single-projection, and all-layers approaches, for the automated segmentation of retinal vessels within SD-OCT volumes of mice. Twenty SD-OCT volumes (400 × 400 × 1024 voxels) from the right eyes of twenty mice were obtained using a Bioptigen SD-OCT machine (Morrisville, NC) to evaluate our methods. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves of the all-layers approach, 0.93, was significantly larger than the AUC for the single-projection (0.91) and baseline (0.88) approach with p < 0.05

Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya.

OBJECTIVE: Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya. PARTICIPANTS: Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]). METHODS: First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR. MAIN OUTCOME MEASURES: The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard. RESULTS: Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. CONCLUSIONS: In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading

Multimodal Graph-Theoretic Approach for Segmentation of the Internal Limiting Membrane at the Optic Nerve Head

In this work, we present a multimodal multiresolution graph-based method to segment the top surface of the retina called the internal limiting membrane (ILM) within optic-nerve-head-centered spectral-domain optical coherence tomography (SD-OCT) volumes. Having a precise ILM surface is crucial as this surface is utilized for measuring several structural parameters such as Bruch’s membrane opening-minimum rim width (BMO-MRW) and cup volume. The proposed method addresses the common current segmentation errors due to the presence of retinal blood vessels, deep cupping, or a very steep slope of the ILM. In order to resolve these issues, the volume is resampled using a set of gradient vector flow (GVF) based columns. The GVF field is computed according to an initial surface segmentation which is obtained through a multiresolution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior information are incorporated in a graph-theoretic approach to compute the ILM segmentation. The method is tested on the SD-OCT volumes from 44 glaucoma subjects and significantly smaller errors were obtained than that from current approaches

Exploring offshore sediment evidence of the 1755 CE Tsunami (Faro, Portugal): implications for the study of outer shelf Tsunami deposits

Outer shelf sedimentary records are promising for determining the recurrence intervals of tsunamis. However, compared to onshore deposits, offshore deposits are more difficult to access, and so far, studies of outer shelf tsunami deposits are scarce. Here, an example of studying these deposits is presented to infer implications for tsunami-related signatures in similar environments and potentially contribute to pre-historic tsunami event detections. A multidisciplinary approach was performed to detect the sedimentary imprints left by the 1755 CE tsunami in two cores, located in the southern Portuguese continental shelf at water depths of 58 and 91 m. Age models based on 14C and 210Pbxs allowed a probable correspondence with the 1755 CE tsunami event. A multi-proxy approach, including sand composition, grain-size, inorganic geochemistry, magnetic susceptibility, and microtextural features on quartz grain surfaces, yielded evidence for a tsunami depositional signature, although only a subtle terrestrial signal is present. A low contribution of terrestrial material to outer shelf tsunami deposits calls for methodologies that reveal sedimentary structures linked to tsunami event hydrodynamics. Finally, a change in general sedimentation after the tsunami event might have influenced the signature of the 1755 CE tsunami in the outer shelf environment.FCT: UID/0350/2020/ UIDB/50019/2020/ SFRH/BD/147685/2019; EC (FP7), MOWER project “Rasgos Erosivos Y Depósitos Arenosos Generados Por La Mow Alrededor De Iberia: Implicaciones Paleoceanográficas, Sedimentarias Y Económicas”(CTM 2012—39599—C03).info:eu-repo/semantics/publishedVersio

An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images

Diabetic patients have a high risk of developing diabetic retinopathy (DR), which is one of the major causes of blindness. With early detection and the right treatment patients may be spared from losing their vision. We propose a computer-aided detection system, which uses retinal fundus images as input and it detects all types of lesions that define diabetic retinopathy. The aim of our system is to assist eye specialists by automatically detecting the healthy retinas and referring the images of the unhealthy ones. For the latter cases, the system offers an interactive tool where the doctor can examine the local lesions that our system marks as suspicious. The final decision remains in the hands of the ophthalmologists. Our approach consists of a multi-class detector, that is able to locate and recognize all candidate DR-defining lesions. If the system detects at least one lesion, then the image is marked as unhealthy. The lesion detector is built on the faster R-CNN ResNet 101 architecture, which we train by transfer learning. We evaluate our approach on three benchmark data sets, namely Messidor-2, IDRiD, and E-Ophtha by measuring the sensitivity (SE) and specificity (SP) based on the binary classification of healthy and unhealthy images. The results that we obtain for Messidor-2 and IDRiD are (SE: 0.965, SP: 0.843), and (SE: 0.83, SP: 0.94), respectively. For the E-Ophtha data set we follow the literature and perform two experiments, one where we detect only lesions of the type micro aneurysms (SE: 0.939, SP: 0.82) and the other when we detect only exudates (SE: 0.851, SP: 0.971). Besides the high effectiveness that we achieve, the other important contribution of our work is the interactive tool, which we offer to the medical experts, highlighting all suspicious lesions detected by the proposed system.<br/