Notice of Retraction: DIEN Network: Detailed Information Extracting Network for Detecting Continuous Circular Capsulorhexis Boundaries of Cataracts

Da Han,Lei Wang

doi:10.1109/access.2020.3021490

Abstract

Notice of Retraction: DIEN Network: Detailed Information Extracting Network for Detecting Continuous Circular Capsulorhexis Boundaries of Cataracts

Highlights

Surgical robot has attracted tremendous attention from all fields of the medical industry since its inception
In addition to the boundary of circular capsulorhexis (CCC) obtained through image processing methods to provide clear and visible assistance to the ophthalmologist, the robot’s motion control module simultaneously uses algorithms to limit the force and movement of the multi-degree-of-freedom robot arm, making it able to withstand safety in the eyeball
With the breakthrough progress of deep learning in various fields in recent years [30]–[36], we have considered combining it with clinical medicine to provide powerful assistance to ophthalmologists in artificial cataract surgery

Summary

INTRODUCTION

Surgical robot has attracted tremendous attention from all fields of the medical industry since its inception. The slave end sensing modules need to be able to collect tiny force armor at the ophthalmologist to adjust the surgery in time Influencing devices such as endoscopes require high computational efficiency to ensure the real-time performance of image information. Its structure is similar to the Da Vinci surgical robot, and it is a master-slave composition This structure ensures that the ophthalmologist can collect real-time information of the patient under the microscope while performing the operation, thereby adjusting the surgical method and handling accidents. In addition to the boundary of CCC obtained through image processing methods to provide clear and visible assistance to the ophthalmologist, the robot’s motion control module simultaneously uses algorithms to limit the force and movement of the multi-degree-of-freedom robot arm, making it able to withstand safety in the eyeball. The traditional edge detection methods mainly process the images according to the gradient information of the image to find the target pixel, while deep-learning based approaches rely on neural network’s learning property

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CONCLUSION