Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes

Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image

Diagnosing and monitoring diabetic macular edema: structural and functional tests.

Diabetic macular edema remains a major cause of visual impairment in adults despite the use of intensive glycemic control, photocoagulation therapy and new intravitreal drugs in the treatment of this disease. Although early diagnosis and treatment lead to better results, we still have patients who become legally blind. Therefore, better structural and functional characterization of this disease is necessary in order to customize treatment

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Cone Spacing Correlates With Retinal Thickness and Microperimetry in Patients With Inherited Retinal Degenerations.

PurposeTo determine whether high-resolution retinal imaging measures of macular structure correlate with visual function over 36 months in retinal degeneration (RD) patients and normal subjects.MethodsTwenty-six eyes of 16 RD patients and 16 eyes of 8 normal subjects were studied at baseline; 15 eyes (14 RD) and 11 eyes (6 normal) were studied 36 months later. Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) was used to identify regions of interest (ROIs) with unambiguous cones at baseline to measure cone spacing. AOSLO images were aligned with spectral-domain optical coherence tomography (SD-OCT) and fundus-guided microperimetry results to correlate structure and function at the ROIs. SD-OCT images were segmented to measure inner segment (IS) and outer segment (OS) thickness. Correlations between cone spacing, IS and OS thickness and sensitivity were assessed using Spearman correlation coefficient ρ with bootstrap analyses clustered by person.ResultsCone spacing (ρ = 0.57, P < 0.001) and macular sensitivity (ρ = 0.19, P = 0.14) were significantly correlated with eccentricity in patients. Controlling for eccentricity, cone spacing Z-scores were inversely correlated with IS (ρ = -0.29, P = 0.002) and OS thickness (ρ = -0.39, P < 0.001) in RD patients only, and with sensitivity in normal subjects (ρ = -0.22, P < 0.001) and RD patients (ρ = -0.38, P < 0.001). After 36 months, cone spacing increased (P < 0.001) and macular sensitivity decreased (P = 0.007) compared to baseline in RD patients.ConclusionsCone spacing increased and macular sensitivity declined significantly in RD patients over 36 months. High resolution images of cone structure correlated with retinal sensitivity, and may be appropriate outcome measures for clinical trials in RD

Unique Contractile and Structural Protein Expression in Dog Ileal Inner Circular Smooth Muscle

This study was designed to test the hypothesis that there is heterogeneous expression of contractile and structural proteins between the smooth muscle cells (SMCs) in the inner and outer circular muscle (ICM and OCM) layers of the ileum. Immunohistochemical staining and quantitation of fresh frozen sections of the dog ileum was performed using protein specific antibodies. Smooth muscle (SM) SMA myosin heavy chain (MHC), α- and γ-SM actin, and vinculin all show greater expression in the ICM relative to the OCM. SMB MHC and fibronectin show the opposite pattern, with greater expression in the OCM relative to the ICM. Differences in expression of these proteins are consistent with proposed differences in function of these muscle layers. Hypotheses regarding muscle tone and the coordination and regulation of peristalsis via these different muscle layers based on this data can now be made and tested

Clinical characterization of 66 patients with congenital retinal disease due to the deep-intronic c.2991+1655A>G mutation in CEP290

Purpose: To describe the phenotypic spectrum of retinal disease caused by the c.2991+1655A>G mutation in CEP290 and to compare disease severity between homozygous and compound heterozygous patients. Methods: Medical records were reviewed for best-corrected visual acuity (BCVA), age of onset, fundoscopy descriptions. Foveal outer nuclear layer (ONL) and ellipsoid zone (EZ) presence was assessed using spectral-domain optical coherence tomography (SD-OCT). Differences between compound heterozygous and homozygous patients were analyzed based on visual performance and visual development. Results: A total of 66 patients were included. The majority of patients had either light perception or no light perception. In the remaining group of 14 patients, median BCVA was 20/195 Snellen (0.99 LogMAR; range 0.12-1.90) for the right eye, and 20/148 Snellen (0.87 LogMAR; range 0.22-1.90) for the left. Homozygous patients tended to be more likely to develop light perception compared to more severely affected compound heterozygous patients (P = 0.080) and are more likely to improve from no light perception to light perception (P = 0.022) before the age of 6 years. OCT data were available in 12 patients, 11 of whom had retained foveal ONL and EZ integrity up to 48 years (median 23 years) of age. Conclusions: Homozygous patients seem less severely affected compared to their compound-heterozygous peers. Improvement of visual function may occur in the early years of life, suggesting a time window for therapeutic intervention up to the approximate age of 17 years. This period may be extended by an intact foveal ONL and EZ on OCT

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe

Anatomo-functional correspondence in the superior temporal sulcus

The superior temporal sulcus (STS) is an intriguing region both for its complex anatomy and for the multiple functions that it hosts. Unfortunately, most studies explored either the functional organization or the anatomy of the STS only. Here, we link these two aspects by investigating anatomo-functional correspondences between the voice-sensitive cortex (Temporal Voice Areas) and the STS depth. To do so, anatomical and functional scans of 116 subjects were processed such as to generate individual surface maps on which both depth and functional voice activity can be analyzed. Individual depth profiles of manually drawn STS and functional profiles from a voice localizer (voice > non-voice) maps were extracted and compared to assess anatomo-functional correspondences. Three major results were obtained: first, the STS exhibits a highly significant rightward depth asymmetry in its middle part. Second, there is an anatomo-functional correspondence between the location of the voice-sensitive peak and the deepest point inside this asymmetrical region bilaterally. Finally, we showed that this correspondence was independent of the gender and, using a machine learning approach, that it existed at the individual level. These findings offer new perspectives for the understanding of anatomo-functional correspondences in this complex cortical region