International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies


:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

ISSN 2228-9860
eISSN 1906-9642



  • Towards Autonomous Micropipette Positioning in Eye Surgery by Employing Deep Learning Algorithm in Micro-Cannulation

    Mukesh Madanan, Nurul Akhmal Mohd Zulkefli (Department of Computer Science, Dhofar University, Salalah, OMAN.).

    Discipline: Artificial Intelligence, Healthcare, Applied Information Technology

    ➤ FullText

    doi: 10.14456/ITJEMAST.2023.1

    Keywords:Artificial Intelligence;Machine Learning;Deep Learning;Robotic Surgery;Eye Surgery;Micro-cannulation;Enhanced Guassian Filtering;Bee Colony Optimization;CNN;Image Processing

    Eye surgery, more precisely the retinal micro-surgery involves both sensory as well as motor skills. This is confined within human boundaries along with physiological limits for maintaining consistent steadiness, the ability to feel small forces and accuracy. Despite these assumptions to leverage robots in all types of surgery, multitudes of challenges have to be confronted to reach complete development. The deployment of robotic assistance in ophthalmologic surgery also faces the same challenge. This work focuses on the autonomous positioning of a micropipette that is to be mounted on a surgical robot for performing eye surgery. Initially, multiple microscopic images of the given micropipette along with its shadow are collected. These images are treated or filtered by using the Enhanced Gaussian Filtering (EGF) method. The so-obtained filtered image is partitioned or segmented by Bee Colony Optimization (BCO) into three segments: micropipette, eye ground and shadow of the micropipette. A new Modified Convolutional Neural Network (MCNN) is leveraged by the robot to perform eye surgery that learns the microscopic images with their ground truth. This MCNN uses automatic feature extraction and estimates micropipette regions with their shadow by examining a microscopic image and its tip. This is tapped for developing autonomous position control in robots. The selected micropipette is found to be positioned at a 99.56% success rate with a mean distance of 1.37 mm from the eye ground that is simulated.

    Paper ID: 14A1A

    Cite this article:

    Madanan, M., Zulkefli, N. A. (2023). Towards Autonomous Micropipette Positioning in Eye Surgery by Employing Deep Learning Algorithm in Micro-Cannulation. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 14(1), 14A1A, 1-20. http://TUENGR.COM/V14/14A1A.pdf DOI: 10.14456/ITJEMAST.2023.1


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