Article

Context specific descriptors for tracking deforming tissue

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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 obtains examples of tissue deformation used to train the classifier. Additional training data is generated by geometric and appearance modelling. 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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... In addition, depending on the applications (for example 3D depth recovery), it is desirable that the tracked features span over the field of view. Currently, research in the field of feature tracking on MIS images [12][13][14][15][16][17][18] faces many problems essentially due to their image properties. Unlike synthetic scenes and outdoor environments, MIS images exhibit considerable drawbacks: specular reflection, shadow effects, dynamic lighting conditions, non-rigid http://dx.doi.org/10.1016/j.compmedimag.2014. ...
... It is generally sensitive to the pair detector and tracking algorithms. Previous work [15,14,17,13] usually compares the number of detected points using different detectors with their default parameters and reports the percentage of robust ones. Generally, the compared sets do not have the same number of points at the detection step. ...
... Other approaches [19,14,15] evaluate the tracking performance using a 3D rigid model or phantom. They compute the registration error between the scanned 3D model and the surface reconstructed from the tracked points using Structure From Motion or Stereovision. ...
... These conditions may result in tracking failure. Several research studies attempt [13,21,20,16] to identify the best features to track from endoscopic images using optical flow and/or feature descriptor methods. Nevertheless, they have not yet resulted in a routinely used clinical application due to MIS image properties. ...
... Evaluation of the Detection Quality: Previous work [21,13,16] usually compares the number of detected points for different detectors and reports the percentage of the robust ones. No quantitative analysis to measure the distribution of points is provided and it do not investigate the choice of algorithm parameter values. ...
... In addition, manual labelling could be only done on salient features, which is difficult for the liver due to its homogeneous surface texture. Other approaches [8,13,21] evaluate the tracking performance using a 3D rigid model or phantom. They compute the registration error between the reconstructed surface (using Structure From Motion or Stereo) from the tracked points and the scanned 3D model. ...
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... They have considered tissue tracking as a classification problem and solved it using decision trees. They have argued that the proposed method is robust to drift, occlusion, orientation and scalingvariations (Mountney & Yang, 2012). Since the classifier trains in a supervised manner, the generalization ability for tissue deformation tracking may be reduced and for new deformations that are not seen in the training set, it may fail to recognize them. ...
... Ground truth segmentation of specular reflection has been performed manually. (Lahane et al., 2012) Unsafe action detection 900 image as train set 213 image as test set (positive and negative classes) (Doignon et al., 2005) segmentation of surgical instruments Images from 3 video sequences of 500 color images (Climent & Mars, 2010) Instrument localization 128 images extracted from a real operation video (Sa-Ing et al., 2012) Instrument tracking simulated videos for different situations (Zhao, 2014) 3D visual tracking of laparoscopic instruments a plastic phantom with a test-bench inside (Collins et al., 2011) deformable 3D surface reconstruction Simulating a 3D kidney model comprising 1820 vertices (Mountney & Yang, 2012) tracking deforming tissue simulated and in vivo MIS datasets of the liver, heart and abdomen (Grasso et al., 2009) Laparoscopic Video Summarization 378 images randomly selected from five laparoscopic cholecystectomy videos (Iakovidis et al., 2010) Endoscopic video summarization annotated video frames with ground truth information labeled manually (Oh et al., 2007) Informative/ non-informative frame classification in endoscopic videos 70 frames were selected as a test set from three colonoscopy videos consisting of 35 informative frames and 35 non-informative frames (Wang et al., 2016) Laparoscopic video summarization and key-frame selection a new gastroscopic video dataset has been proposed from 30 volunteers with more than 400k images (Zappella et al., 2013) Surgical gesture classification Previously presented dataset Many researches have tried to address some of the mentioned issues in their work. But, there is still need to propose more robust and stable methods for any of the mentioned applications in laparoscopic image processing. ...
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Laparoscopy is a minimally-invasive surgery using a few small incisions on the patient’s body to insert the tools and telescope and conduct the surgical operation. Laparoscopic video processing can be used to extract valuable knowledge and help the surgeons. We discuss the present and possible future role of processing laparoscopic videos. The various applications are categorized for image processing algorithms in laparoscopic surgeries including preprocessing video frames by laparoscopic image enhancement, telescope related applications (telescope position estimation, telescope motion estimation and compensation), surgical instrument related applications (surgical instrument detection and tracking), soft tissue related applications (soft tissue segmentation and deformation tracking) and high level applications such as safe actions in laparoscopic videos, summarization of laparoscopic videos, surgical task recognition and extracting knowledge using fusion techniques. Some different methods have been proposed previously for each of the mentioned applications using image processing.
... To the best of our knowledge, none of these has yet been applied to endoscopic stitching, despite their high potential. An exception is the work of Mountney et al. [107], [110] (2008, 2011), who have presented an online learning scheme for feature descriptors and adapted the method of randomized trees for keypoint recognition by Lepetit et al. [86] (2006) to develop contextspecific descriptors for application in laparoscopic stereoscopy SLAM. They have also presented a comparison of feature descriptors for MIS [104] (2007). ...
... This is true for all stitching and reconstruction methods that include a global optimization step, such as bundle adjustment. The SLAM methods, primarily applied in laparoscopy, are an exception to this -as presented by Mountney and Yang's group [104]- [110] as a stereoscopic approach, as well as by Grasa et al. [58]- [60] for monoscopic views (see section II-D). Bouma et al. [26] have also presented a realtime reconstruction approach for minimally invasive surgery, which incorporates stereoscopic ego-motion computation in real-time. ...
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... Puerto-Souza and Mariottini (2013) introduced a Hierarchical Multi-Affine (HMA) algorithm to map features between two endoscopic images, allowing to recover features that were lost after a complete occlusion or sudden camera motions. Mountney and Yang (2012) exploited online 85 learning and classification using a context specific feature descriptor, in order to increase the robustness against drift and occlusion. Du et al. (2015) used a triangular geometric mesh model to combine features and intensity information to robustly track soft tissue surface deformation. ...
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... A comparative evaluation of state-of-the-art feature-matching algorithms for endoscopic images has been carried out in [186]. [155,187,188,205,231,268], deforming tissue tracking is a very hard research challenge that still requires a lot of further work. Endoscopic videos feature many domain-induced problems like scarcity of distinctive landmarks because of homogenous surfaces and indistinctive texture that makes it hard to find good points to track. ...
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Performing motion tracking in real-time is an old and recurrent problem in Computer Vision. It has been addressed through a large set of approaches [17], [9], [1], but achieving a high level of robustness is still a challenge, especially with low definition input. In the considered application, tracking the heart motion in endoscopic beating heart sequences, the sensitivity of existing algorithms to visual artifacts and variations in illumination is an issue that calls for improvements. In the prospect of developing a motion compensation architecture for robotically assisted beating heart surgery, we address the problem of visual information retrieval by proposing a new Composite Tracking Algorithm using both template matching and texture analysis. As we will show in this paper, the use of texture characterization of the heart surface improves the overall precision and robustness, in comparison with other prior approaches.
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Texture features are widely used for image classification and retrieval. They offer an efficient way to add prior knowledge in image processing, using an appropriate prior parametrization. In this paper, we present the methods we used to select among numerous existing features the most adapted to deal with beating heart tracking, using an experimental database of images and data mining techniques. Then we introduce the way we plan to use this information to reinforce region tracking algorithms in order to track the motion of characteristic landmarks on the heart surface
Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid (June 2003) recently evaluated a variety of approaches and identified the SIFT [D. G. Lowe, 1999] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply principal components analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.
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The 3D reconstruction problem from a single endoscope image of a smooth object is studied in the context of the shape from shading methods and considering a single light source at the camera projection center. Based on a curve expansion shape from shading algorithm, a spherical projection model for the endoscope camera and a dichromatic model for the surface reflectance, an approach to solve practical problems, namely the endoscope image distortion and the removal of the image specular reflection component, is presented. Results obtained from application of this approach to synthetic and real images are presented
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A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.
No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments. 1 Introduction IEEE Conference on Computer Vision and Pattern Recognition (CVPR94) Seattle, June 1994 Is feature tracking a solved problem? The extensive studies of image correlation [4], [3], [15], [18], [7], [17] and sum-of-squared-difference (SSD...
Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online Gaze-Contingent Soft Tissue Deformation Tracking for Minimally Invasive Robotic Surgery, Medical Image Computing and Computer Assisted Intervention
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An Iterative Image Registration Technique with an Application to Stereo Vision A performance evaluation of local descriptors
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Simulated Experiment with Known Ground Truth To quantitatively evaluate the performance of the proposed method with respect to deformation, a simulation was performed to generate synthetic data. A laparoscopic image of 6
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