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Dakua S.P.,Qatar Robotic Surgery Center
International Journal of Pattern Recognition and Artificial Intelligence | Year: 2015

High-level noise and low contrast characteristics in medical images continue to present major bottlenecks in their segmentation despite increased imaging modalities. This paper presents a semi-automatic algorithm that utilizes the noise for enhancing the contrast of low contrast input magnetic resonance images followed by a new graph cut method to reconstruct the surface of left ventricle. The main contribution in this work is a new formulation for preventing the conventional cellular automata method to leak into surrounding regions of similar intensity. Instead of segmenting each slice of a subject sequence individually, we empirically select a few slices, segment them, and reconstruct the left ventricular surface. During the course of surface reconstruction, we use level sets to segment the rest of the slices automatically. We have throughly evaluated the method on both York and MICCAI Grand Challenge workshop databases. The average Dice coefficient (in %) is found to be 92.4 ± 1.3 (value indicates the mean and standard deviation) whereas false positive ratio, false negative ratio, and specificity are found to be 0.019, 7.62 × 10-3, and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.94 mm. The encouraging quantitative and qualitative results reflect the potential of the proposed method. © 2015 World Scientific Publishing Company. Source


Bernhardt S.,University of British Columbia | Abi-Nahed J.,Qatar Robotic Surgery Center | Abugharbieh R.,University of British Columbia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Robotic assistance in minimally invasive surgical interventions has gained substantial popularity over the past decade. Surgeons perform such operations by remotely manipulating laparoscopic tools whose motion is executed by the surgical robot. One of the main tools deployed is an endoscopic binocular camera that provides stereoscopic vision of the operated scene. Such surgeries have notably garnered wide interest in renal surgeries such as partial nephrectomy, which is the focus of our work. This operation consists of the localization and removal of tumorous tissue in the kidney. During this procedure, the surgeon would greatly benefit from an augmented reality view that would display additional information from the different imaging modalities available, such as pre-operational CT and intra-operational ultrasound. In order to fuse and visualize these complementary data inputs in a pertinent way, they need to be accurately registered to a 3D reconstruction of the imaged surgical scene topology captured by the binocular camera. In this paper we propose a simple yet powerful approach for dense matching between the two stereoscopic camera views and for reconstruction of the 3D scene. Our method adaptively and accurately finds the optimal correspondence between each pair of images according to three strict confidence criteria that efficiently discard the majority of outliers. Using experiments on clinical in-vivo stereo data, including comparisons to two state-of-the-art 3D reconstruction techniques in minimally invasive surgery, our results illustrate superior robustness and better suitability of our approach to realistic surgical applications. © 2013 Springer-Verlag. Source


Lombaert H.,McGill University | Peyrat J.-M.,Qatar Robotic Surgery Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Cardiac fiber architecture plays an important role in electrophysiological and mechanical functions of the heart. Yet, its inter-subject variability and more particularly, its relationship to the shape of the myocardium, is not fully understood. In this paper, we extend the statistical analysis of cardiac fiber architecture beyond its description with a fixed average geometry. We study the co-variation of fiber architecture with either shape or strain-based information by exploring their principal modes of joint variations. We apply our general framework to a dataset of 8 ex vivo canine hearts, and find that strain-based information appears to correlate best with the fiber architecture. Furthermore, compared to current approaches that warp an average atlas to the patient geometry, our preliminary results show that joint statistics improves fiber synthesis from shape by 8.0%, with cases up to 25.9%. Our experiments also reveal evidence on a possible relation between architectural variability and myocardial thickness. © 2013 Springer-Verlag. Source


Amir-Khalili A.,University of British Columbia | Peyrat J.-M.,Qatar Robotic Surgery Center | Hamarneh G.,Simon Fraser University | Abugharbieh R.,University of British Columbia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

With the advent of robot-assisted laparoscopic surgery (RALS), intra-operative stereo endoscopy is becoming a ubiquitous imaging modality in abdominal interventions. This high resolution intra-operative imaging modality enables the reconstruction of 3D soft-tissue surface geometry with the help of computer vision techniques. This reconstructed surface is a prerequisite for many clinical applications such as image-guidance with cross-modality registration, telestration, expansion of the surgical scene by stitching/mosaicing, and collision detection. Reconstructing the surface geometry from camera information alone remains a very challenging problem in RALS mainly due to a small baseline between the optical centres of the cameras, presence of blood and smoke, specular highlights, occlusion, and smooth/textureless regions. In this paper, we propose a method for increasing the overall surface reconstruction accuracy by incorporating patient specific shape priors extracted from pre-operative images. Our method is validated on an in silico phantom and we show that the combination of both pre-operative and intra-operative data significantly improves surface reconstruction as compared to the ground truth. Finally, we verify the clinical potential of the proposed method in the context of abdominal surgery in a phantom study of an ex vivo lamb kidney. © 2013 Springer-Verlag. Source


Amir-Khalili A.,University of British Columbia | Nosrati M.S.,Simon Fraser University | Peyrat J.-M.,Qatar Robotic Surgery Center | Hamarneh G.,Simon Fraser University | Abugharbieh R.,University of British Columbia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In most robot-assisted surgical interventions, multimodal fusion of pre- and intra-operative data is highly valuable, affording the surgeon a more comprehensive understanding of the surgical scene observed through the stereo endoscopic camera. More specifically, in the case of partial nephrectomy, fusing pre-operative segmentations of kidney and tumor with the stereo endoscopic view can guide tumor localization and the identification of resection margins. However, the surgeons are often unable to reliably assess the levels of trust they can bestow on what is overlaid on the screen. In this paper, we present the proof-of-concept of an uncertainty-encoded augmented reality framework and novel visualizations of the uncertainties derived from the pre-operative CT segmentation onto the surgeon's stereo endoscopic view. To verify its clinical potential, the proposed method is applied to an ex vivo lamb kidney. The results are contrasted to different visualization solutions based on crisp segmentation demonstrating that our method provides valuable additional information that can help the surgeon during the resection planning. © 2013 Springer-Verlag. Source

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