Surgical skills can be improved by continuous surgical training and feedback, thus reducing adver... more Surgical skills can be improved by continuous surgical training and feedback, thus reducing adverse outcomes while performing an intervention. With the advent of new technologies, researchers now have the tools to analyze surgical instrument motion to differentiate surgeons’ levels of technical skill. Surgical skills assessment is time-consuming and prone to subjective interpretation. The surgical instrument detection and tracking algorithm analyzes the image captured by the surgical robotic endoscope and extracts the movement and orientation information of a surgical instrument to provide surgical navigation. This information can be used to label raw surgical video datasets that are used to form an action space for surgical skill analysis. Instrument detection and tracking is a challenging problem in MIS, including robot-assisted surgeries, but vision-based approaches provide promising solutions with minimal hardware integration requirements. This study offers an overview of the de...
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Path planning of a tool in Minimally Invasive Surgery (MIS) can provide assistance to the surgeon... more Path planning of a tool in Minimally Invasive Surgery (MIS) can provide assistance to the surgeons by giving solutions for faster and safe tool movements during the surgery. However, the main challenge in this problem is to address non-uniform tool shape for planning that can change due to the tool’s dexterity. A typical robotic path planning approach by describing the robot’s feasible movements using C-space is applied in this work. Unlike the robotic path planning problem, the C-space description capturing the movement does not give any closed-form solution due to high degree of freedom associated with the tool moved by human hands. Hence, an interval-based approach is used for describing the C-space. The proposed interval-based approach is capable of dividing the space into feasible and non-feasible intervals of different sizes which helps to reduce the search area and cover the obstacles in a refined manner. This paper presents collision-free and fast path computation using interval arithmetic between any two points in a 2D- surgical environment cluttered with obstacles for a surgical tool robot.
Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tum... more Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that brings plethora of solutions in this research prospect. In this paper, we designed a network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) using residual and mirroring principles. RescueNet uses unpaired adversarial training to segment the whole tumor followed by core and enhance regions in a brain MRI scan. The problem in automatic brain tumor analysis is preparing large scale labeled data for training of deep networks which is a time consuming and tedious task. To eliminate this need of paired data we used unpaired training approach to train the proposed network. Performance evaluation parameters are taken as DICE and Sensitivity measure. The experimental results are tested on BraTS 2015 and BraTS 2017 [1] dataset and the result outperforms the existing methods for brain tumor segmentation. The combination of domain-specific segmentation methods and general-purpose adversarial learning loomed to leverage huge advantages for medical imaging applications and can improve the ability of automated algorithms to assist radiologists.
Surgical skills can be improved by continuous surgical training and feedback, thus reducing adver... more Surgical skills can be improved by continuous surgical training and feedback, thus reducing adverse outcomes while performing an intervention. With the advent of new technologies, researchers now have the tools to analyze surgical instrument motion to differentiate surgeons’ levels of technical skill. Surgical skills assessment is time-consuming and prone to subjective interpretation. The surgical instrument detection and tracking algorithm analyzes the image captured by the surgical robotic endoscope and extracts the movement and orientation information of a surgical instrument to provide surgical navigation. This information can be used to label raw surgical video datasets that are used to form an action space for surgical skill analysis. Instrument detection and tracking is a challenging problem in MIS, including robot-assisted surgeries, but vision-based approaches provide promising solutions with minimal hardware integration requirements. This study offers an overview of the de...
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Path planning of a tool in Minimally Invasive Surgery (MIS) can provide assistance to the surgeon... more Path planning of a tool in Minimally Invasive Surgery (MIS) can provide assistance to the surgeons by giving solutions for faster and safe tool movements during the surgery. However, the main challenge in this problem is to address non-uniform tool shape for planning that can change due to the tool’s dexterity. A typical robotic path planning approach by describing the robot’s feasible movements using C-space is applied in this work. Unlike the robotic path planning problem, the C-space description capturing the movement does not give any closed-form solution due to high degree of freedom associated with the tool moved by human hands. Hence, an interval-based approach is used for describing the C-space. The proposed interval-based approach is capable of dividing the space into feasible and non-feasible intervals of different sizes which helps to reduce the search area and cover the obstacles in a refined manner. This paper presents collision-free and fast path computation using interval arithmetic between any two points in a 2D- surgical environment cluttered with obstacles for a surgical tool robot.
Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tum... more Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that brings plethora of solutions in this research prospect. In this paper, we designed a network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) using residual and mirroring principles. RescueNet uses unpaired adversarial training to segment the whole tumor followed by core and enhance regions in a brain MRI scan. The problem in automatic brain tumor analysis is preparing large scale labeled data for training of deep networks which is a time consuming and tedious task. To eliminate this need of paired data we used unpaired training approach to train the proposed network. Performance evaluation parameters are taken as DICE and Sensitivity measure. The experimental results are tested on BraTS 2015 and BraTS 2017 [1] dataset and the result outperforms the existing methods for brain tumor segmentation. The combination of domain-specific segmentation methods and general-purpose adversarial learning loomed to leverage huge advantages for medical imaging applications and can improve the ability of automated algorithms to assist radiologists.
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Papers by Shubhangi Nema