Titlebook: Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures; 4th International Wo M. Jorge Cardoso,Tal Arbel,Raj Shekhar Co

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废止

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Discrete

Design Issues Related to Text-Based Tasksrained region. Evaluation of the proposed method on 40 volumes of . bovine tissue shows . detection precision, . recall rate, mean classification time per slice of 0.06 s and mean tip localization error of . mm. The promising results indicate potential of the method for further evaluation on clinical pain management procedures.

Adjourn

Shape-Based Pose Estimation of Robotic Surgical Instrumentsse-specific end-effector appearance. Experimental results show that the proposed method can effectively estimate the end-effector pose and delineate its boundary while being trained with moderately sized data clusters. We then show that matching such huge ensemble of templates takes less than one second on commodity hardware.

缩影

Local Phase-Based Learning for Needle Detection and Localization in 3D Ultrasoundrained region. Evaluation of the proposed method on 40 volumes of . bovine tissue shows . detection precision, . recall rate, mean classification time per slice of 0.06 s and mean tip localization error of . mm. The promising results indicate potential of the method for further evaluation on clinical pain management procedures.

哭得清醒了

Fully Automatic Detection of Distal Radius Fractures from Posteroanterior and Lateral Radiographstically locate the outline of the radius in both posteroanterior (PA) and lateral (LAT) radiographs, then use shape and texture features to classify abnormalities. We show for the first time that fractures can be better identified in the lateral view, and that combining information from both views leads to an overall improvement in performance.

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Shape-Based Pose Estimation of Robotic Surgical Instrumentsed, rigid appearance templates. The templates, which are equipped with pose-related keypoints and segmentation masks, allow for explicit pose estimation and segmentation of multiple end-effectors as well as fine-grained non-maximum suppression. We train the templates by grouping examples of end-effe