Neuropath planner–automatic path searching for neurosurgery

Fujii, Tetsuya; Emoto, Hiroshi; Sugou, Nobuo; Mito, Toshiaki; Shibata, Iekado

doi:10.1016/s0531-5131(03)00363-7

Cited by 21 publications

(28 citation statements)

References 3 publications

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“…Recent research has compared commercial optical and electromagnetic computer-assisted navigation systems for fluoroscopic targeting [1] and has evaluated electromagnetic tracking systems for various clinical applications [2]. The preliminary results on electromagnetic fluoroscopy-based navigation are mixed, with [1] reporting no significant difference between optical and electromagnetic tracking in a laboratory setup, and [2] reporting large errors and calling for caution and a procedure-specific analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The preliminary results on electromagnetic fluoroscopy-based navigation are mixed, with [1] reporting no significant difference between optical and electromagnetic tracking in a laboratory setup, and [2] reporting large errors and calling for caution and a procedure-specific analysis. Our purpose is to further study these issues when the fluoroscope is tracked in real time.…”

Section: Resultsmentioning

confidence: 99%

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Surgical Navigation

2009

Int J CARS

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Surgical Navigation

2009

Int J CARS

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“…Some only visualize the resulting path, others can also render target lesions and virtual tools. On the other hand, Bourbakis and Awad [4], Fujii et al [5], and Vaillant et al [6] describe elaborate path calculation algorithms. However, these require the manual assignment of tissue importance values, which is not very intuitive.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Trajectory Planning for Deep Brain Stimulation: A Feasibility Study

Brunenberg

Vilanova

Visser‐Vandewalle³

et al. 2007

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. DBS for Parkinson's disease involves an extensive planning to find a suitable electrode implantation path to the selected target. We have investigated the feasibility of improving the conventional planning with an automatic calculation of possible paths in 3D. This requires the segmentation of anatomical structures. Subsequently, the paths are calculated and visualized. After selection of a suitable path, the settings for the stereotactic frame are determined. A qualitative evaluation has shown that automatic avoidance of critical structures is feasible. The participating neurosurgeons estimate the time gain to be around 30 minutes.

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“…In reality, however, it is very expensive to have more than two modality data acquired for each patient. In addition, a method of automatic neurosurgical path searching is described at [5], which integrates the structure of blood vesssels obtained from MRA and brain tissues. Their path searching machanism consists of a voting scheme by assiging each region assigned with a number between 1 to 4 according to the importance of the region; i.e., a smaller number can be given to a region that is further from blood vessels, by which the regions with minimum values are classified as the safest paths.…”

Section: Introductionmentioning

confidence: 99%

3D CBIR with sparse coding for image-guided neurosurgery

Hui

Gao

2013

Signal Processing

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an open access repository of Middlesex University research http://eprints.mdx.ac.uk Qian, Yu; Hui, Rui; Gao, Xiaohong W., 2012. 3D CBIR with sparse coding for image-guided neurosurgery. Available from Middlesex University's Research Repository. Copyright:Middlesex University Research Repository makes the University's research available electronically.Copyright and moral rights to this work are retained by the author and/or other copyright owners. No part of the work may be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s). A copy may be downloaded for personal, noncommercial, research or study without prior permission and without charge. Any use of the work for private study or research must be properly acknowledged with reference to the work's full bibliographic details.This work may not be reproduced in any format or medium, or extensive quotations taken from it, or its content changed in any way, without first obtaining permission in writing from the copyright holder(s).If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address: eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract: This research takes an application-specific approach to investigate, extend and implement the state of the art in the fields of both visual information retrieval and machine learning, bridging the gap between theoretical models and real world applications. During an image-guided neurosurgery, path planning remains the foremost and hence the most important step to perform an operation and ensures the maximum resection of an intended target and minimum sacrifice of health tissues. In this investigation, the technique of content-based image retrieval (CBIR) coupled with machine learning algorithms are exploited in designing a computer aided path planning system (CAP) to assist junior doctors in planning surgical paths while sustaining the highest precision. Specifically, after evaluation of approaches of sparse coding and K-means in constructing a codebook, the model of sparse codes of 3D SIFT has been furthered and thereafter employed for retrieving, The novelty of this work lies in the fact that not only the existing algorithms for 2D images have been successfully extended into 3D space, leading to promising results, but also the application of CBIR, that is mainly in a research realm, to a clinical sector can be achieved by th...

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Neuropath planner–automatic path searching for neurosurgery

Cited by 21 publications

References 3 publications

Surgical Navigation

Surgical Navigation

Automatic Trajectory Planning for Deep Brain Stimulation: A Feasibility Study

3D CBIR with sparse coding for image-guided neurosurgery

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