Segmentation and 3D Reconstruction Approaches for the Design of Laparoscopic Augmented Reality Environments

Sánchez-González, Patricia; Gayá, Francisco; Cano, Alicia; Gómez, Enrique J.

doi:10.1007/978-3-540-70521-5_14

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…A split and merge technique is chosen as the evolution strategy in the method. The method is based on a simplified illumination model with light source and camera systems located in the same spatial point (30). An illumination equation enables the definition of an optimal feature to distinguish structures, which is the gradient of the logarithm of image intensity.…”

Section: Organs'' Trackingmentioning

confidence: 99%

Laparoscopic video analysis for training and image-guided surgery

Sánchez-González

Cano

García

et al. 2011

Minimally Invasive Therapy & Allied Technologies

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Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery.tool tracking, organ reconstruction

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Section: Organs'' Trackingmentioning

confidence: 99%

Laparoscopic video analysis for training and image-guided surgery

Sánchez-González

Cano

García

et al. 2011

Minimally Invasive Therapy & Allied Technologies

Self Cite

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“…Considering negligible the indirect light intensity, it can be proved that the simplified illumination equation of the surgical scene is [11]:…”

Section: Anatomic Shape Reconstructionmentioning

confidence: 99%

“…These reconstruction techniques are named shape-from-X approaches, and use cues like motion, stereo, shading, focus, defocus, contours, etc. for extracting the shape of the objects [8][9][10][11]. In spite of the numerous techniques, not one of them is able to solve all 3D reconstruction issues in laparoscopic surgery without drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Video-endoscopic image analysis for 3D reconstruction of the surgical scene

et al. 2009

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-Video-endoscopic image analysis is an important source of information for extracting relevant 3D information of the surgical scene in minimally invasive surgery. This work proposes a new approach for getting depth information and relative position between instruments and organs based on video image analysis. The proposed image analysis extracts information from: (1) the projective geometry of the surgical setting in the image to determine laparoscopic tool's pose, and (2) the scene illumination captured in the image for getting a depth-map of the surgical scene. Results have verified the potential of video-analysis based methods for obtaining 3D information of the whole surgical scene. This information offers new possibilities for developing new augmented reality surgical applications and for improving surgical training with objective metrics. The main benefit of this approach is the lack of extra elements which disturb surgeons' performance in the clinical routine.

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“…Thus, data extraction is performed either from a human sight or from sensor devices. In this context, different methods have been recently used for data acquisition: patient specific procedures description [2][3][4], interview of the surgeons [5], sensor-based methods [6][7][8][9][10][11][12][13][14][15][16], using fixed protocols created by expert surgeons [17], or combination between them [18].…”

Section: Introductionmentioning

confidence: 99%

“…In their work, they focused on risk situation for surgical assistance. Finally, Sanchez-Gonzales et al [16] extracted useful information from videos such as 3D map to help surgeons performing operating techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

Lalys

Riffaud

Morandi

et al. 2010

Information Processing in Computer-Assisted Interventions

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Abstract. The segmentation of the surgical workflow might be helpful for providing context-sensitive user interfaces, or generating automatic report. Our approach focused on the automatic recognition of surgical phases by microscope image classification. Our workflow, including images features extraction, image database labelisation, Principal Component Analysis (PCA) transformation and 10-fold cross-validation studies was performed on a specific type of neurosurgical intervention, the pituitary surgery. Six phases were defined by an expert for this type of intervention. We thus assessed machine learning algorithms along with the data dimension reduction. We finally kept 40 features from the PCA and found a best correct classification rate of the surgical phases of 82% with the multiclass Support Vector Machine.

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Segmentation and 3D Reconstruction Approaches for the Design of Laparoscopic Augmented Reality Environments

Cited by 6 publications

References 5 publications

Laparoscopic video analysis for training and image-guided surgery

Laparoscopic video analysis for training and image-guided surgery

Video-endoscopic image analysis for 3D reconstruction of the surgical scene

Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification

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