Titlebook: Computer-Assisted and Robotic Endoscopy; Third International Terry Peters,Guang-Zhong Yang,Jonathan McLeod Conference proceedings 2017 Spr

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书目名称Computer-Assisted and Robotic Endoscopy影响因子(影响力)




书目名称Computer-Assisted and Robotic Endoscopy影响因子(影响力)学科排名




书目名称Computer-Assisted and Robotic Endoscopy网络公开度




书目名称Computer-Assisted and Robotic Endoscopy网络公开度学科排名




书目名称Computer-Assisted and Robotic Endoscopy被引频次




书目名称Computer-Assisted and Robotic Endoscopy被引频次学科排名




书目名称Computer-Assisted and Robotic Endoscopy年度引用




书目名称Computer-Assisted and Robotic Endoscopy年度引用学科排名




书目名称Computer-Assisted and Robotic Endoscopy读者反馈




书目名称Computer-Assisted and Robotic Endoscopy读者反馈学科排名

火海

The European Community and ASEAN,acy according to overlap size. In contrast to the conventional mosaicking approach, the proposed approach can produce panoramic image even in the case of 0% inter-cameras overlap. Additionally, the proposed approach is fast enough for clinical use.

instate

记忆

Toussaint Houeninvo,Philippe Sèdédjiution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

起波澜

Preoperative Diagnostic Procedures,y deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.

Transfusion

Transfusion

CLASP

鸣叫

ORBSLAM-Based Endoscope Tracking and 3D Reconstruction,ution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

谄媚于性

Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking,y deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.