تقسیم بندی ابزار بلادرنگ در جراحی رباتیک
ترجمه نشده

تقسیم بندی ابزار بلادرنگ در جراحی رباتیک

عنوان فارسی 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
مجله/کنفرانس: نامه های رباتیک و اتوماسیون - Robotics and Automation Letters
رشته های تحصیلی مرتبط: مهندسی پزشکی، برق
گرایش های تحصیلی مرتبط: بیوالکتریک، بیومکانیک، رباتیک
کلمات کلیدی فارسی: 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
نمایه: Master Journal List - Scopus
شناسه دیجیتال (DOI): https://doi.org/10.1109/LRA.2019.2900854
دانشگاه: Mobarakol Islam NUS Graduate School for Integrative Sciences and Engineering and Department of Biomedical Engineering, National University of Singapore, Singapore
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
شناسه ISSN: 2377-3766
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13053
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I- Introduction


II- Proposed Method


III- Experiment


IV- Results


V- Discussion and Conclusion


References

نمونه متن انگلیسی 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]–[5] 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]–[12] from tissue are the key focus in the field of RMIS.

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