Semi‐autonomous image‐guided brain tumour resection using an integrated robotic system: A bench‐top study

Hu, Danying; Gong, Yuanzheng; Seibel, Eric J.; Sekhar, Laligam N.; Hannaford, Blake

doi:10.1002/rcs.1872

Cited by 25 publications

(22 citation statements)

References 31 publications

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“…RetinaMatch may be used in other medical image applications for template matching. For example, in the case of endoscopic guidance of therapy by a surgical robot [42], the current limited-sized FOV can be matched onto the panorama for endoscope localization. Thus, this image template matching technique can be used to create a more reliable closed-loop control for the robot arm and surgical tool guidance.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology

Gong

Erichson

Kelly

et al. 2019

IEEE Trans. Med. Imaging

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Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this work, we develop an efficient and accurate retinal matching technique that combines dimension reduction and mutual information (MI), called RetinaMatch. The dimension reduction initializes the MI optimization as a coarse localization process, which narrows the optimization domain and avoids local optima. The effectiveness of RetinaMatch is demonstrated on the open fundus image database STARE with simulated reduced FOV and anticipated degradations, and on retinal images acquired by adapter-based optics attached to a smartphone. RetinaMatch achieves a success rate over 94% on human retinal images with the matched target registration errors below 2 pixels on average, excluding the observer variability. It outperforms the standard template matching solutions. In the application of measuring vessel diameter repeatedly, single pixel errors are expected. In addition, our method can be used in the process of image mosaicking with area-based registration, providing a robust approach when the feature based methods fail. To the best of our knowledge, this is the first template matching algorithm for retina images with small template images from unconstrained retinal areas. In the context of the emerging mixed reality market, we envision automated retinal image matching and registration methods as transformative for advanced teleophthalmology and long-term retinal monitoring.

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Section: Conclusion and Discussionmentioning

confidence: 99%

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology

Gong

Erichson

Kelly

et al. 2019

IEEE Trans. Med. Imaging

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“…Early attempts on implementing HRI in BTs were made in [16], [17], where a surgical robot is controlled by BT. The robot execution halts before performing the tumor ablation and user input is required to decide one of the two possible ablation paths to proceed.…”

Section: Related Workmentioning

confidence: 99%

Interactive Disambiguation for Behavior Tree Execution

Iovino¹,

Doğan²,

Leite³

et al. 2022

Preprint

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In recent years, robots are used in an increasing variety of tasks, especially by small-and medium sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask followup clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available 1 .

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“…Papers Workflow and human activity modelling [39], [68], [63], [10], [69], [13], [151] Camera Control [132], [98] Smart homes and Power Grids [49], [15], [18], [60] Engineering Design Tasks [175], [121] was used to get input for a Fallback node, to take advantage of an experienced operator's (brain surgeon) expertise in choosing the correct action for a brain tumor ablation task. This has been demonstrated in both simulation [68] and in real experiments on mice [69].…”

Section: Areamentioning

confidence: 99%