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

Mobile-PolypNet: Lightweight Colon Polyp Segmentation Network for Low-Resource Settings

Colon polyps, small clump of cells on the lining of the colon, can lead to colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps automatically is crucial in the prevention of CRC. The deep learning models proposed for the detection and segmentation of colorectal polyps are resource-consuming. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation by using most of the common image-segmentation metrics. The backbone model achieved a DICE score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset, improving the accuracy by 4.12% and 5.12%, respectively, over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model\u27s performance but it was not significant (-value = 0.815)

Mobile-CellNet: Automatic Segmentation of Corneal Endothelium Using an Efficient Hybrid Deep Learning Model

Purpose: The corneal endothelium, the innermost layer of the human cornea, exhibits a morphology of predominantly hexagonal cells. These endothelial cells are believed to have limited regeneration capacity, and their density decreases over time. Endothelial cell density (ECD) can therefore be used to measure the health of the corneal endothelium and the overall cornea. In clinical settings, specular microscopes are used to image this layer. Owing to the unavailability of reliable automatic tools, technicians often manually mark the cell centers and borders to measure ECD for such images, a process that is time and resource-consuming. Methods: In this article, we propose Mobile-CellNet, a novel completely automatic, efficient deep learning-based cell segmentation algorithm to estimate ECD. This uses 2 similar image segmentation models working in parallel along with image postprocessing using classical image processing techniques. We also compare the proposed algorithm with widely used biomedical image segmentation networks U-Net and U-Net++. Results: The proposed technique achieved a mean absolute error of 4.06% for the ECD on the test set, comparable with the error for U-Net of 3.80% (P = 0.185 for difference), but requiring almost 31 times fewer floating-point operations (FLOPs) and 34 times fewer parameters. Conclusions: Mobile-CellNet accurately segments corneal endothelial cells and reports ECD and cell morphology efficiently. This can be used to develop tools to analyze specular corneal endothelial images in remote settings

Alcohol Abuse Is Associated With Alterations in Corneal Endothelial Cell Morphology

PURPOSE: Alcohol consumption is highly prevalent throughout the world. We sought to detect, in a large sample of cornea donors, whether alcohol abuse is associated with changes in corneal endothelial morphology after accounting for other comorbidities including tobacco use. METHODS: At a single eye bank, 10,322 eyes from a total of 5624 unique donors underwent imaging with a Konan CellChek D specular microscope. Demographic information and medical history were associated with each tissue. Images were analyzed using a standardized protocol for assessment of endothelial cell density, hexagonality, and variation. In this retrospective analysis, a multivariable regression was conducted to assess for an association between alcohol abuse and corneal endothelial metrics. Measurements were averaged across eyes for each donor. Bonferroni corrections were applied to account for multiple comparisons. RESULTS: Among 5624 donors, the mean (standard deviation) endothelial cell density was 2785 (383.0) cells/mm2. Indicators of alcohol abuse were present in 1382 donors (24.5%). In a multivariable regression model that included age, sex, tobacco use, history of cataract surgery, and diabetes mellitus, alcohol abuse was associated with a decrease of 60.9 cells/mm2 [95% confidence interval (CI), -83.0 to -38.7 cells/mm2, P = 7.6 × 10-8], an increase in the coefficient of variation by 0.0048 (95% CI, 0.17-0.79, P = 0.002), and a decrease in percent hexagonality by 0.93% (95% CI, -1.3 to -0.6, P = 4.5 × 10-7). CONCLUSIONS: Alcohol abuse is associated with significant alterations to corneal endothelial density and morphology

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-