Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing

Indirect ophthalmoscopy (IO) is the standard of care for evaluation of the neonatal retina. When recorded on video from a head-mounted camera, IO images have low quality and narrow Field of View (FOV). We present an image fusion methodology for converting a video IO recording into a single, high quality, wide-FOV mosaic that seamlessly blends the best frames in the video. To this end, we have developed fast and robust algorithms for automatic evaluation of video quality, artifact detection and removal, vessel mapping, registration, and multi-frame image fusion. Our experiments show the effectiveness of the proposed methods

Vision based robot assistance in TTTS fetal surgery

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents an accurate and robust tracking vision algorithm for Fetoscopic Laser Photo-coagulation (FLP) surgery for Twin-Twin Transfusion Syndrome (TTTS). The aim of the proposed method is to assist surgeons during anastomosis localization, coagulation and review using a tele-operated robotic system. The algorithm computes the relative position of the fetoscope tool tip with respect to the placenta, via local vascular structure registration.The algorithm uses image features (local superficial vascular structures of the placenta’s surface) to automatically match consecutive fetoscopic images. It is composed of three sequential steps: image processing (filtering, binarization and vascular structures segmentation); relevant Points Of Interest (POIs) seletion; and image registration between consecutive images.The algorithm has to deal with the low quality of fetoscopic images, the liquid and dirty environment inside the placenta jointly with the thin diameter of the fetoscope optics and low amount of environment light reduces the image quality. The obtained images are blurred, noisy and with very poor color components.The tracking system has been tested using real video sequences of FLP surgery for TTTS. The computational performance enables real time tracking, locally guiding the robot over the placenta’s surface with enough accuracy.Peer ReviewedPostprint (author's final draft

Estimation of Gestational Age via Image Analysis of Anterior Lens Capsule Vascularity in Preterm Infants: A Pilot Study

Introduction: Anterior lens capsule vascularity (ALCV) is resorbed in the developing fetus from 27 to 35 weeks gestation. In this pilot study, we evaluated the feasibility and validity of combining smartphone ophthalmoscope videos of ALCV and image analysis for gestational age estimation.Methods: ALCV videos were captured longitudinally in preterm neonates from delivery using a PanOptic® Ophthalmoscope with an iExaminer® adapter (Welch-Allyn). ALCV video frames were manually selected and quantified using semi-automatic image analysis. A predictive model based on ALCV features was compared to gold-standard ultrasound gestational age estimates.Results: A total of 64 image-capture sessions were carried out in 24 neonates. Ultrasound-estimated gestational age and ALCV-predicted gestational age estimates indicate that the two methods are similar (r = 0.78, p < 0.0001). ALCV estimates of gestational age were within 0.11 ± 1.3 weeks of ultrasound estimates. In the final model, gestational age was predicted within ± 1 week for 54% and within ± 2 weeks for 86% of the measures.Conclusions: This novel application of smartphone ophthalmoscopy and ALCV image analysis may provide a safe, accurate and non-invasive technology to estimate postnatal gestational age, especially in low income countries where gestational age may not be known at birth

Image analytic tools for tissue characterization using optical coherence tomography

Optical coherence tomography (OCT) has been emerging as a promising imaging technique, with a strong capability of non-invasive, in vivo, high resolution, depth-resolved imaging. There is a great potential to use OCT to guide the treatment of arrhythmias, to prevent preterm birth, and to detect breast cancer. To facilitate the clinical applications, this thesis presents three image analytic tools to characterize biological tissue: 1) automated fiber direction analysis; 2) automated volumetric stitching; 3) automated tissue classification. The fiber direction analysis consists of a particle-filter-based 3D tractography scheme and a pixel-wise fiber analysis scheme. The stitching algorithm enlarges the field of view of current OCT system from millimeter to centimeter level by volumetric stitching using scale-invariant feature transform. Based on relevance vector machine, a region-based classification scheme and a grid-based classification scheme are developed to automatically identify tissue composition in human cardiac tissue and human breast tissue. These tools are collaboratively used to study OCT images from cardiac, cervical, and breast tissue. In cardiac tissue, we apply the fiber orientation analysis to reconstruct 3D cardiac myofibers tractography and perform pixel-wise fiber analysis on the collagen region within human heart. In addition, we apply the region-based algorithm to segment and classify tissue compositions, such as collagen, adipose tissue, fibrotic myocardium, and normal myocardium, over a single or a stitched OCT volume. Using our algorithm, we observe fiber directionality change over depths and find that the fiber orientation changes more dramatically in atria than in ventricle. We also observe different dispersion patterns within collagen layer. In cervical tissue, our stitching algorithm enables a paramount 3D view of entire axial slices. Together with pixel-wise fiber orientation scheme, we analyze the difference of dispersion property within inner/outer regions of four quadrants. We observe two dispersion patterns in pregnant and non-pregnant cervical tissue at the location close to upper cervix. In addition, we discover that an increasing trend of dispersion and an increasing trend of penetration depth from internal orifice (os) to external os. In breast tissue, we visualize various features in both benign and malignant tissues such as invasive ductal carcinoma (IDC), ductal carcinoma in situ, cyst, and terminal duct lobule unit in stitched OCT images. Focusing on the automated detection of IDC, we propose a hierarchy framework of classification model and apply our classifier in two OCT systems and achieve both reasonable sensitivity and specificity in identifying cancerous region

Graph Theory and Dynamic Programming Framework for Automated Segmentation of Ophthalmic Imaging Biomarkers

<p>Accurate quantification of anatomical and pathological structures in the eye is crucial for the study and diagnosis of potentially blinding diseases. Earlier and faster detection of ophthalmic imaging biomarkers also leads to optimal treatment and improved vision recovery. While modern optical imaging technologies such as optical coherence tomography (OCT) and adaptive optics (AO) have facilitated in vivo visualization of the eye at the cellular scale, the massive influx of data generated by these systems is often too large to be fully analyzed by ophthalmic experts without extensive time or resources. Furthermore, manual evaluation of images is inherently subjective and prone to human error.</p><p>This dissertation describes the development and validation of a framework called graph theory and dynamic programming (GTDP) to automatically detect and quantify ophthalmic imaging biomarkers. The GTDP framework was validated as an accurate technique for segmenting retinal layers on OCT images. The framework was then extended through the development of the quasi-polar transform to segment closed-contour structures including photoreceptors on AO scanning laser ophthalmoscopy images and retinal pigment epithelial cells on confocal microscopy images. </p><p>The GTDP framework was next applied in a clinical setting with pathologic images that are often lower in quality. Algorithms were developed to delineate morphological structures on OCT indicative of diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME). The AMD algorithm was shown to be robust to poor image quality and was capable of segmenting both drusen and geographic atrophy. To account for the complex manifestations of DME, a novel kernel regression-based classification framework was developed to identify retinal layers and fluid-filled regions as a guide for GTDP segmentation.</p><p>The development of fast and accurate segmentation algorithms based on the GTDP framework has significantly reduced the time and resources necessary to conduct large-scale, multi-center clinical trials. This is one step closer towards the long-term goal of improving vision outcomes for ocular disease patients through personalized therapy.</p>Dissertatio

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Diffuse Reflectance Spectroscopy to Quantify In Vivo Tissue Optical Properties: Applications in Human Epithelium and Subcutaneous Murine Colon Cancer

Colorectal cancer is the 4th most common and 2nd deadliest cancer. Problems exist with predicting which patients will respond best to certain therapy regimens. Diffuse reflectance spectroscopy has been suggested as a candidate to optically monitor a patient’s early response to therapy and has been received favorably in experimentally managing other cancers such as breast and skin. In this dissertation, two diffuse reflectance spectroscopy probes were designed: one with a combined high-resolution microendoscopy modality, and one that was optimized for acquiring data from subcutaneous murine tumors. For both probes, percent errors for estimating tissue optical properties (reduced scattering coefficient and absorption coefficient) were less than 5% and 10%, respectively. Then, studies on tissue-simulating phantoms were performed to test probe sensitivity and to serve as testing platforms for investigators in biomedical optics. Next, the diffuse reflectance spectroscopy probe was applied to subcutaneous murine colon tumors (n=61) undergoing either antibody immunotherapy or standard 5-fluorouracil chemotherapy. Mice treated with a combination of these therapies showed reduced tumor growth compared to saline control, isotype control, immunotherapy, and chemotherapy groups (p\u3c0.001, \u3c0.001, \u3c0.001, and 0.046, respectively) 7 days post-treatment. Additionally, at 7 days post-treatment, oxyhemoglobin, a marker currently being explored as a functional prognostic cancer marker, trended to increase in immunotherapy, chemotherapy, and combination therapy groups compared to controls (p=0.315, 0.149, and 0.190). Also of interest, an oxyhemoglobin flare (averageincrease of 1.44x from baseline, p=0.03 compared to controls) was shown in tumors treated with chemotherapy, indicating that diffuse reflectance spectroscopy may be useful as a complimentary tool to monitor early tumor therapeutic response in colon cancer. However, subject-to-subject variability was high and studies correlating survival to early oxyhemoglobin flares are suggested