Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

He, Yucheng; Zhang, Peng; Qi, Xiaozhi; Zhao, Baoliang; Li, Shibo; Hu, Ying

doi:10.1109/access.2020.2967474

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…This is due the fact that those false positives did not appear in all the models at the same regions; therefore, the use of ensembles eliminates them from the final result. This is of great importance in the clinical practice, given that false positive classifications during endoluminal inspection might result in a range of complications of the surgical operation, including tools colliding with tissues [ 17 ], incorrect path planning [ 18 ], among others.…”

Section: Discussionmentioning

confidence: 99%

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

et al. 2021

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Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

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Section: Discussionmentioning

confidence: 99%

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

et al. 2021

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“…This is due the fact that those false positives did not appear in all the models at the same regions, therefore, the use of ensembles eliminate them from the final result. This is of great importance in the clinical practice, given that false positive classifications during endoluminal inspection might results in a range of complications of the surgical operation, including tools colliding with tissues [17], incorrect path planning [18], among others. Despite the positive results achieved by the proposed approach, some limitations are worth to be mentioned.…”

Section: Discussionmentioning

confidence: 99%

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

Lazo¹,

Marzullo²,

Moccia³

et al. 2021

Preprint

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Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on Convolutional Neural Networks (CNNs). Methods: The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks(m1) and Mask-RCNN(m2), which are fed with single still-frames I(t). The other two models (M1, M2) are modifications of the former ones consisting on the addition of a stage which makes use of 3D Convolutions to process temporal information. M1, M2 are fed with triplets of frames (I(t − 1), I(t), I(t + 1)) to produce the segmentation for I(t). Results: The proposed method was evaluated using a custom dataset of 11 videos (2,673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion:The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in presence of poor visibility, occasional bleeding, or specular reflections.

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“…Therefore, to ensure the safety of surgical operations, surgical path planning and navigation are important. In, 56 a binarized three‐dimensional grid map was created based on the patient's CT images, and endoscopic surgical approaches were searched and optimized using the A‐star algorithm. A KARL STORZ NAV1 SinusTracker navigation software incorporating augmented reality elements was investigated in 57 .…”

Section: Navigation and Control Of Crs For Essmentioning

confidence: 99%

Continuum robots for endoscopic sinus surgery: Recent advances, challenges, and prospects

Cao

Shi

Hong

et al. 2022

Robotics Computer Surgery

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Purpose Endoscopic sinus surgery (ESS) has been recognized as an effective treatment modality for paranasal sinus diseases. Over the past decade, continuum robots (CRs) for ESS have been studied, but there are still some challenges. This paper presents a review on the scientific studies of CRs for ESS. Methods Based on the analysis of the anatomical structure of the paranasal sinus, the requirements of CRs for ESS are discussed. Recent studies on rigid robots, handheld flexible robots, and CRs for ESS are presented. Surgical path planning, navigation, and control are also included. Results Concentric tube CRs and cable‐driven CRs have great potential for applications in ESS. The CRs incorporated with multiple replaceable arms with different functions are preferable in ESS. Conclusion Further study on navigation and control is required to improve the performance of CRs for ESS.

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Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

Cited by 15 publications

References 32 publications

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

Continuum robots for endoscopic sinus surgery: Recent advances, challenges, and prospects

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