Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs’ dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 µm 2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.This work has been partly funded by Ministerio de Ciencia, Tecnología e Innovación, Colombia, Project 124489786239 (Contract 763-2021), Universidad Tecnológica de Bolívar (UTB) Project CI2021P02, and Agencia Estatal de Investigación del Gobierno de España (PID2020-114582RB-I00/ AEI / 10.13039/501100011033). J. Sierra thanks UTB for a post-graduate scholarship.Peer ReviewedPostprint (published version

Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was designed to directly target the volumes to estimate, not requiring any labeled segmentation on the images. The network was based on a 3D U-net with extra layers defined in a scan-ning module that learned features like the circularity of the objects and the volumes to estimate in a weakly-supervised manner. The only targets defined were the left ventricle volumes and the circularity of the object detected through the estimation of the pi value derived from its shape. We had access to 397 cases corresponding to 397 different subjects. We randomly selected 98 cases to use as test set. Results: The results show a good match between the real and estimated volumes in the test set, with a mean relative error of 8% and a mean absolute error of 9.12 ml with a Pearson correlation coefficient of 0.95. The derived segmentations obtained by the network achieved Dice coefficients with a mean value of 0.79. Conclusions: The proposed method is capable of obtaining the left ventricle volume biomarker in the end-diastole and offer an explanation of how it obtains the result in the form of a segmentation mask without the need of segmentation labels to train the algorithm, making it a potentially more trustworthy method for clinicians and a way to train neural networks more easily when segmentation labels are not readily available.The authors acknowledge financial support from the Consel-leria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029) , from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085) , and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars2, actuacion Interempresas Internacional) , Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP-20192020) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Pérez-Pelegrí, L.; Maceira, AM.; Bodi, V.; Moratal, D. (2021). Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology. Computer Methods and Programs in Biomedicine. 208:1-8. https://doi.org/10.1016/j.cmpb.2021.106275S1820

Artificial Intelligence Algorithms for Eye Banking

Eye banking plays a critical role in modern medicine by providing cornea tissues for transplantation to restore vision for millions of people worldwide. The evaluation of corneal endothelium is done by measuring the corneal endothelial cell density (ECD). Unfortunately, the current system to measure ECD is manual, time-consuming, and error prone. Furthermore, the impact of social behaviors and biological conditions on corneal endothelium and corneal transplant success is largely unexplored. To overcome these challenges, this dissertation aims to develop tools for corneal endothelial image and data analysis that enhance the efficiency and quality of the cornea transplants. In the first study, an image processing algorithm is developed to analyze corneal endothelial images captured by a Konan CellChek specular microscope. The algorithm successfully identifies the region of interest, filters the image, and employs stochastic watershed segmentation to determine cell boundaries and evaluate endothelial cell density (ECD). The proposed algorithm achieves a high correlation with manual counts (R2 = 0.98) and has an average analysis time of 2.5 seconds. In the second study, a deep learning-based cell segmentation algorithm called Mobile-CellNet is proposed to estimate ECD. This technique addresses the limitations of classical algorithms and creates a more robust and highly efficient algorithm. The approach achieves a mean absolute error of 4.06% for ECD on the test set, similar to U-Net but with significantly fewer floating-point operations and parameters. The third study explores the correlation between alcohol abuse and corneal endothelial morphology in a donor pool of 5,624 individuals. Multivariable regression analysis shows that alcohol abuse is associated with a reduction in endothelial cell density, an increase in the coefficient of variation, and a decrease in percent hexagonality. These studies highlight the potential of big data and artificial algorithms in accurately and efficiently analyzing corneal images and donor medical data to improve the efficiency of eye banking and patient outcomes. By automating the analysis of corneal images and exploring the impact of social behaviors and biological conditions on corneal endothelial morphology, we can enhance the quality and availability of cornea transplants and ultimately improve the lives of millions of people worldwide

Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks

Quantifying shape changes in the ciliary muscle during accommodation is essential in understanding the potential role of the ciliary muscle in presbyopia. The ciliary muscle can be imaged in-vivo using OCT but quantifying the ciliary muscle shape from these images has been challenging both due to the low contrast of the images at the apex of the ciliary muscle and the tedious work of segmenting the ciliary muscle shape. We present an automatic-segmentation tool for OCT images of the ciliary muscle using fully convolutional networks. A study using a dataset of 1,039 images shows that the trained fully convolutional network can successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. The study also shows that EfficientNet outperforms other current backbones of the literature

Development of Novel Diagnostic Tools for Dry Eye Disease using Infrared Meibography and In Vivo Confocal Microscopy

Dry eye disease (DED) is a multifactorial disease of the ocular surface where tear film instability, hyperosmolarity, neurosensory abnormalities, meibomian gland dysfunction, ocular surface inflammation and damage play a dedicated etiological role. Estimated 5 to 50% of the world population in different demographic locations, age and gender are currently affected by DED. The risk and occurrence of DED increases at a significant rate with age, which makes dry eye a major growing public health issue. DED not only impacts the patient’s quality of vision and life, but also creates a socio-economic burden of millions of euros per year. DED diagnosis and monitoring can be a challenging task in clinical practice due to the multifactorial nature and the poor correlation between signs and symptoms. Key clinical diagnostic tests and techniques for DED diagnosis include tearfilm break up time, tear secretion – Schirmer’s test, ocular surface staining, measurement of osmolarity, conjunctival impression cytology. However, these clinical diagnostic techniques are subjective, selective, require contact, and are unpleasant for the patient’s eye. Currently, new advances in different state-of-the-art imaging modalities provide non-invasive, non- or semi-contact, and objective parameters that enable objective evaluation of DED diagnosis. Among the different and constantly evolving imaging modalities, some techniques are developed to assess morphology and function of meibomian glands, and microanatomy and alteration of the different ocular surface tissues such as corneal nerves, immune cells, microneuromas, and conjunctival blood vessels. These clinical parameters cannot be measured by conventional clinical assessment alone. The combination of these imaging modalities with clinical feedback provides unparalleled quantification information of the dynamic properties and functional parameters of different ocular surface tissues. Moreover, image-based biomarkers provide objective, specific, and non / marginal contact diagnosis, which is faster and less unpleasant to the patient’s eye than the clinical assessment techniques. The aim of this PhD thesis was to introduced deep learning-based novel computational methods to segment and quantify meibomian glands (both upper and lower eyelids), corneal nerves, and dendritic cells. The developed methods used raw images, directly export from the clinical devices without any image pre-processing to generate segmentation masks. Afterward, it provides fully automatic morphometric quantification parameters for more reliable disease diagnosis. Noteworthily, the developed methods provide complete segmentation and quantification information for faster disease characterization. Thus, the developed methods are the first methods (especially for meibomian gland and dendritic cells) to provide complete morphometric analysis. Taken together, we have developed deep learning based automatic system to segment and quantify different ocular surface tissues related to DED namely, meibomian gland, corneal nerves, and dendritic cells to provide reliable and faster disease characterization. The developed system overcomes the current limitations of subjective image analysis and enables precise, accurate, reliable, and reproducible ocular surface tissue analysis. These systems have the potential to make an impact clinically and in the research environment by specifying faster disease diagnosis, facilitating new drug development, and standardizing clinical trials. Moreover, it will allow both researcher and clinicians to analyze meibomian glands, corneal nerves, and dendritic cells more reliably while reducing the time needed to analyze patient images significantly. Finally, the methods developed in this research significantly increase the efficiency of evaluating clinical images, thereby supporting and potentially improving diagnosis and treatment of ocular surface disease

GAN-Based Super-Resolution And Segmentation Of Retinal Layers In Optical Coherence Tomography Scans

Optical Coherence Tomography (OCT) has been identified as a noninvasive and cost-effective imaging modality for identifying potential biomarkers for Alzheimer\u27s diagnosis and progress detection. Current hypotheses indicate that retinal layer thickness, which can be assessed via OCT scans, is an efficient biomarker for identifying Alzheimer\u27s disease. Due to factors such as speckle noise, a small target region, and unfavorable imaging conditions manual segmentation of retina layers is a challenging task. Therefore, as a reasonable first step, this study focuses on automatically segmenting retinal layers to separate them for subsequent investigations. Another important challenge commonly faced is the lack of clarity of the layer boundaries in retina OCT scans, which compels the research of super-resolving the images for improved clarity. Deep learning pipelines have stimulated substantial progress for the segmentation tasks. Generative adversarial networks (GANs) are a prominent field of deep learning which achieved astonishing performance in semantic segmentation. Conditional adversarial networks as a general-purpose solution to image-to-image translation problems not only learn the mapping from the input image to the output image but also learn a loss function to train this mapping. We propose a GAN-based segmentation model and evaluate incorporating popular networks, namely, U-Net and ResNet, in the GAN architecture with additional blocks of transposed convolution and sub-pixel convolution for the task of upscaling OCT images from low to high resolution by a factor of four. We also incorporate the Dice loss as an additional reconstruction loss term to improve the performance of this joint optimization task. Our best model configuration empirically achieved the Dice coefficient of 0.867 and mIOU of 0.765

Improving cataract surgery procedure using machine learning and thick data analysis

Cataract surgery is one of the most frequent and safe Surgical operations are done globally, with approximately 16 million surgeries conducted each year. The entire operation is carried out under microscopical supervision. Even though ophthalmic surgeries are similar in some ways to endoscopic surgeries, the way they are set up is very different. Endoscopic surgery operations were shown on a big screen so that a trainee surgeon could see them. Cataract surgery, on the other hand, was done under a microscope so that only the operating surgeon and one more trainee could see them through additional oculars. Since surgery video is recorded for future reference, the trainee surgeon watches the full video again for learning purposes. My proposed framework could be helpful for trainee surgeons to better understand the cataract surgery workflow. The framework is made up of three assistive parts: figuring out how serious cataract surgery is; if surgery is needed, what phases are needed to be done to perform surgery; and what are the problems that could happen during the surgery. In this framework, three training models has been used with different datasets to answer all these questions. The training models include models that help to learn technical skills as well as thick data heuristics to provide non-technical training skills. For video analysis, big data and deep learning are used in many studies of cataract surgery. Deep learning requires lots of data to train a model, while thick data requires a small amount of data to find a result. We have used thick data and expert heuristics to develop our proposed framework.Thick data analysis reduced the use of lots of data and also allowed us to understand the qualitative nature of data in order to shape a proposed cataract surgery workflow framework

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

IntroductionAccurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.MethodsThe proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.ResultsExperimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.DiscussionOur method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem