مقاله انگلیسی رایگان در مورد تقسیم بندی ابزار بلادرنگ در جراحی رباتیک با استفاده از یادگیری تخاصمی عمیق نظارت شده – ۲۰۱۹ IEEE

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله تقسیم بندی ابزار بلادرنگ در جراحی رباتیک با استفاده از یادگیری تخاصمی عمیق نظارت شده کمکی
عنوان انگلیسی مقاله Real-Time Instrument Segmentation in Robotic Surgery Using Auxiliary Supervised Deep Adversarial Learning
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
مقاله بیس این مقاله بیس میباشد
نمایه (index) Master Journal List – Scopus
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
شناسه ISSN ۲۳۷۷-۳۷۶۶
مدل مفهومی دارد
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متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی پزشکی، برق
گرایش های مرتبط بیوالکتریک، بیومکانیک، رباتیک
نوع ارائه مقاله
ژورنال
مجله نامه های رباتیک و اتوماسیون – Robotics and Automation Letters
دانشگاه Mobarakol Islam NUS Graduate School for Integrative Sciences and Engineering and Department of Biomedical Engineering, National University of Singapore, Singapore
کلمات کلیدی یادگیری عمیق در رباتیک و اتوماسیون، ردیابی بصری، تشخیص اجسام، تقسیم بندی و طبقه بندی
کلمات کلیدی انگلیسی Deep learning in robotics and automation، visual tracking، object detection، segmentation and categorization
شناسه دیجیتال – doi
https://doi.org/10.1109/LRA.2019.2900854
کد محصول E13053
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فهرست مطالب مقاله:
Abstract

I- Introduction

II- Proposed Method

III- Experiment

IV- Results

V- Discussion and Conclusion

References

 

بخشی از متن مقاله:

Abstract

Robot-assisted surgery is an emerging technology that has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics, and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provides contextual information about the different tissues and instruments being operated with. For this purpose, we have developed a light-weight cascaded convolutional neural network to segment the surgical instruments from high-resolution videos obtained from a commercial robotic system. We propose a multi-resolution feature fusion module to fuse the feature maps of different dimensions and channels from the auxiliary and main branch. We also introduce a novel way of combining auxiliary loss and adversarial loss to regularize the segmentation model. Auxiliary loss helps the model to learn low-resolution features, and adversarial loss improves the segmentation prediction by learning higher order structural information. The model also consists of a light-weight spatial pyramid pooling unit to aggregate rich contextual information in the intermediate stage. We show that our model surpasses existing algorithms for pixelwise segmentation of surgical instruments in both prediction accuracy and segmentation time of high-resolution videos.

INTRODUCTION

ROBOT-ASSISTED minimally invasive surgery (RMIS) has revolutionized the practice of surgery by optimizing surgical procedures, improving dexterous manipulations and enhancing patient safety [1]. Recent developments in the field of robotics, vision and smaller instruments have impacts on minimally invasive intervention. The common extensively used surgical robotic system is the Da Vinci Xi robot [2]–[۵] enable remote control laparoscopic surgery with long kinematic chains. The Raven II [6] is a robust surgical system consists of spherical positioning mechanisms. Remarkable recent surgical tools with complex actuation systems utilized micro-machined super-elastic tool [7] and concentric tubes [8]. However, with the reduction in size and complex actuation mechanisms, control of the instruments and cognitive representation of the robot kinematics are forthwith remarkably challenging in a surgical scenario. In addition, there are factors that complicate the surgical environment such as shadows and specular reflections, partial occlusion, smoke, and body fluid as well as the dynamic nature of background tissues. Hence, real-time surgical instruments detection, tracking, and isolation [9]–[۱۲] from tissue are the key focus in the field of RMIS.

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