Images of photoreceptors in living primate eyes using adaptive optics two-photon ophthalmoscopy

In vivo two-photon imaging through the pupil of the primate eye has the potential to become a useful tool for functional imaging of the retina. Two-photon excited fluorescence images of the macaque cone mosaic were obtained using a fluorescence adaptive optics scanning laser ophthalmoscope, overcoming the challenges of a low numerical aperture, imperfect optics of the eye, high required light levels, and eye motion. Although the specific fluorophores are as yet unknown, strong in vivo intrinsic fluorescence allowed images of the cone mosaic. Imaging intact ex vivo retina revealed that the strongest two-photon excited fluorescence signal comes from the cone inner segments. The fluorescence response increased following light stimulation, which could provide a functional measure of the effects of light on photoreceptors

A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network

Purpose: Adaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in images of choroideremia (CHM). We further compare the results to the repeatability and reliability of manual cone identifications in CHM. Methods: We used split-detection adaptive optics scanning laser ophthalmoscopy to image the inner segment cone mosaic of 17 patients with CHM. Cones were manually identified twice by one experienced grader and once by two additional experienced graders in 204 regions of interest (ROIs). An open-source CNN either pre-trained on normal images or trained on CHM images automatically identified cones in the ROIs. True and false positive rates and Dice\u27s coefficient were used to determine the agreement in cone locations between data sets. Interclass correlation coefficient was used to assess agreement in bound cone density. Results: Intra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading. Conclusions: Manual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements. Translational Relevance: These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM

Retinal gene therapy

INTRODUCTION: Inherited retinal diseases are the leading cause of sight impairment in people of working age in England and Wales, and the second commonest in childhood. Gene therapy offers the potential for benefit. SOURCES OF DATA: Pubmed and clinicaltrials.gov. Areas of agreement: Gene therapy can improve vision in RPE65-associated Leber Congenital Amaurosis (RPE65-LCA). Potential benefit depends on efficient gene transfer and is limited by the extent of retinal degeneration. AREAS OF CONTROVERSY: The magnitude of vision improvement from RPE65-LCA gene therapy is suboptimal, and its durability may be limited by progressive retinal degeneration. GROWING POINTS: The safety and potential benefit of gene therapy for inherited and acquired retinal diseases is being explored in a rapidly expanding number of trials. Areas timely for developing research: Developments in vector design and delivery will enable greater efficiency and safety of gene transfer. Optimization of trial design will accelerate reliable assessment of outcomes

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device