Robust 3D Visual Tracking for Robotic-Assisted Cardiac Interventions

Richa, Rogério; Bó, Antônio Padilha Lanari; Poignet, Philippe

doi:10.1007/978-3-642-15705-9_33

Cited by 25 publications

(27 citation statements)

References 9 publications

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“…Most of the prior work focuses on the detection of the instruments used during surgery or in the operating room [13,14,15,16,17,18,19,20] using techniques such as dynamic time warping, support vector machines and HMMs. However, these techniques use only frame-level features, such as color, texture and shape-based cues.…”

Section: Introductionmentioning

confidence: 99%

Surgical Gesture Segmentation and Recognition

Tao

Zappella

Hager

et al. 2013

Lecture Notes in Computer Science

103

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Abstract. Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

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Section: Introductionmentioning

confidence: 99%

Surgical Gesture Segmentation and Recognition

Tao

Zappella

Hager

et al. 2013

Lecture Notes in Computer Science

103

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“…The LK approach was found to be sensitive to illumination changes and has no explicit mechanism for dealing with occlusion. Robustness to occlusion and drift can be improved by incorporating feature redetection as proposed in (Richa et al, 2010). The window size was set constant for all video sequences.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, ad hoc methods, can be susceptible to drift, non-continuous tracking and occlusion. Recently, a hybrid approach has been proposed (Richa et al, 2010) to deal with the weaknesses of individual tracking methods.…”

Section: Introductionmentioning

confidence: 99%

Context specific descriptors for tracking deforming tissue

Mountney¹,

Yang²

2012

Medical Image Analysis

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Abstract.In minimally invasive surgery, deployment of motion compensation, dynamic active constraints and adaptive intra-operative guidance require accurate estimation of deforming tissue in 3D. To this end, the use of vision-based techniques is advantageous in that it does not require the integration of additional hardware to the existing surgical settings.Deformation can be recovered by tracking features on the surface of the tissue. Existing methods are mostly based on ad hoc machine vision techniques that have generally been developed for rigid scenes or use pre-determined models with parameters fine tuned to specific surgical settings. In this work, we propose a novel tracking technique based on a context specific feature descriptor. The descriptor can adapt to its surroundings and identify the most discriminate information for tracking. The feature descriptor is represented as a decision tree and the tracking process is formulated as a classification problem for which log likelihood ratios are used to improve classifier training. A recursive tracking algorithm is used to obtain examples of tissue deformation used to train the classifier. Additional training data is generated by geometric and appearance modeling. Experimental results have shown that the proposed context specific descriptor is robust to drift, occlusion, and changes in orientation and scale. The performance of the algorithm is compared with existing tracking algorithms and validated with both simulated and in vivo datasets.

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“…Many methods have attempted to recover 3D information on the organ from intra-abdominal images [21,27,17,35,29]. In this study, a stereo endoscope is used in order to recover 3D information on the liver.…”

Section: D Motion Estimationmentioning

confidence: 99%

Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery

Haouchine

Dequidt

Peterlík

et al. 2013

2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

119

107

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Figure 1: A sequence of images showing the superimposition of the 3D real-time biomechanical model onto the human liver, undergoing deformation due to instrument interaction during minimally invasive hepatic surgery. The liver is represented in wireframe, the tumor in purple, the hepatic vein is shown in blue and the portal vein in green. ABSTRACTThis paper presents a method for real-time augmentation of vascular network and tumors during minimally invasive liver surgery. Internal structures computed from pre-operative CT scans can be overlaid onto the laparoscopic view for surgery guidance. Compared to state-of-the-art methods, our method uses a real-time biomechanical model to compute a volumetric displacement field from partial three-dimensional liver surface motion. This permits to properly handle the motion of internal structures even in the case of anisotropic or heterogeneous tissues, as it is the case for the liver and many anatomical structures. Real-time augmentation results are presented on in vivo and phantom data and illustrate the benefits of such an approach for minimally invasive surgery.

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Robust 3D Visual Tracking for Robotic-Assisted Cardiac Interventions

Cited by 25 publications

References 9 publications

Surgical Gesture Segmentation and Recognition

Surgical Gesture Segmentation and Recognition

Context specific descriptors for tracking deforming tissue

Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery

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