Automated Localization of Blood Vessels in Retinal Images

Vessel structure is one of the most important parts of the retina which physicians can detect many diseases by analysing its features. Localization of blood vessels in retina images is an important process in medical image analysis. This process is also more challenging with the presence of bright and dark lesions. In this thesis, two automated vessel localization methods to handle both healthy and unhealthy (pathological) retina images are analyzed. Each method consists of two major steps and the second step is the same in the two methods. In the first step, an algorithm is used to decrease the effect of bright lesions. In Method 1, this algorithm is based on K- Means segmentation, and in Method 2, it is based on a regularization procedure. In the second step of both methods, a multi-scale line operator is used to localize the line-shaped vascular structures and ignore the dark lesions which are generally assumed to have irregular patterns. After the introduction of the methods, a detailed quantitative and qualitative comparison of the methods with one another as well as the state-of-the-art solutions in the literature based on the segmentation results on the images of the two publicly available datasets, DRIVE and STARE, is reported. The results demonstrate that the methods are highly comparable with other solutions

Detection of CSR from Blue Wave Fundus Autofluorescence Images using Deep Neural Network Based on Transfer Learning

Fluid clot below the retinal surface is the root cause of Central Serous Retinopathy (CSR), often referred to as Central Serous Chorioretinopathy (CSC). Delicate tissues that absorb sunlight and enable the brain to recognize images make up the retina. This important organ is vulnerable to damage, which could result in blindness and vision loss for the affected person. Therefore, complete visual loss may be reversed and, in some circumstances, may return to normal with early diagnosis discovery. Therefore, timely and precise CSR detection prevents serious damage to the macula and serves as a foundation for the detection of other retinal disorders. Although CSR has been detected using Blue Wave Fundus Autofluorescence (BWFA) images, developing an accurate and efficient computational system is still difficult. This paper focuses on the use of trained Convolutional Neural Networks (CNN) to implement a framework for accurate and automatic CSR recognition from BWFA images. Transfer Learning has been used in conjunction with pre-trained network architectures (VGG19) for classification. Statistical parameter evaluation has been used to investigate the effectiveness of DCNN. For VGG19, the statistic parameters evaluation revealed a classification accuracy of 97.30%, a precision of 99.56%, an F1 score of 97.25%, and a recall of 95.04% when using a BWFA image dataset collected from a local eye hospital in Cochin, Kerala, India. Identification of CSR from BWFA images is not done before. This paper illustrates how the proposed framework might be applied in clinical situations to assist physicians and clinicians in the identification of retinal diseases

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. In contrast to these patch-based approaches, we show that deep spatial autoencoding models can be efficiently used to capture normal anatomical variability of entire 2D brain MR images. A variety of experiments on real MR data containing MS lesions corroborates our hypothesis that we can detect and even delineate anomalies in brain MR images by simply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further improve the segmentation performance over standard deep representation learning

An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy

Individuals with diabetes are more likely to develop Diabetic Retinopathy (DR), a chronic ailment that can lead to blindness if left undiagnosed. Early-stage Diabetic Retinopathy (DR) is characterized by Microaneurysms (MA), which appear as tiny red lesions on the retina. This paper investigates a unique approach for the automated early identification of microaneurysms  in eye images. A unique ensemble classifier technique is suggested in this work. Classifiers like SVM, KNN, Decision Tree, and Naïve Bayes are chosen in this study for building an ensemble model. After preprocessing the image, certain common image characteristics such as shape and intensity features were retrieved from the candidate. The mean absolute difference of each feature is computed. Based on mean ranges that would give improved classification results, an expert classifier is chosen and trained. The outputs of the classifiers are integrated for each of the distinct characteristics, and the number of categories that have been most frequently repeated is utilized to reach a final decision. The process has been comprehensively validated using two available open datasets, like e-ophtha and DIARETDB1. On the e-ophtha and DIARETDB1 datasets, the ensemble model achieved an AUC of 0.928 and 0.873, Sensitivity of 90.7% and 85%, Specificity of 90% and 91% respectively

Zebrafish Eye Development: Rac and the Creepy Crawlers of the Eye

During vertebrate eye development the optic vesicles protrude from either side of the brain and form the optic cups. As an optic cup starts to surround the lens a groove on the ventral side of the eye forms, known as the choroid fissure (CF). Normally, the CF will close around the optic nerve and hyaloid vasculature. If this process does not occur properly it results in a keyhole opening in the eye known as coloboma. This results in blindness and affects nearly 1 in 4-5,000 births. Zebrafish were utilized as a model for eye development to study CF closure (CFC) as they utilize similar gene expression and cellular signaling. Previously, a transient β-catenin/actin fusion seam within the fusing CF was observed indicating the formation of cell-to-cell contacts. Rac, a small G-protein, regulates actin cytoskeleton reorganization and formation of lamellipodia required for cell-to-cell adhesion. These lamellipodia increase interactions between cells increasing contacts that could form adherens junctions. I hypothesized Rac would be expressed prior to CFC and dissipate upon CFC completion, similar to adhesion proteins. To determine Rac’s localization, embryos were cryosectioned at 47 and 49-hours post-fertilization (hpf) and Rac immunofluorescence was observed. These data demonstrated Rac is present within the CF edges at 47 hpf and dissipates the CF fusion seam as CFC progresses in wildtypes embryos around 49 hpf. Quantification of these data further demonstrated a progressive fusion event that initiates in the central section of the CF and moves bidirectionally towards the proximal and distal edges, emulating a zipper-like fashion. in vivo analysis of Rx3:GFP embryos (neuroretina labeled) identified a subpopulation of cells that are present within the CF at 24 hpf. This population of cells appear highly protrusive and “reach” in multiple directions. Further analysis of Rac embryos identified these “reaching cells” as Rac positive. in vivo analysis of this cell population revealed that seven identified categories of reaching cells can be divided into three stages of CFC. Rac is also required for reaching cells. When observing the Rac-DN embryo no reaching cells were ever observed, regardless of heat-shocking time. The Rac-DN embryos showed an abnormal optic cup angle and unusual cuboidal cells shapes (early heat-shock). In later heat-shocked times the abnormal angle and unusual cell shapes were resolved, however, there were unusual division patterns that were observed. Further investigation is ongoing to identify the role of Rac in this cell population and the role of “reaching cells” during zebrafish eye development

An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures