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Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR–iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS.

Methods

A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented.

Results

Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods.

Conclusion

The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

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Acknowledgements

Authors thank the University of Guanajuato for the provided financial support that allows concluding this research. Additionally, we would like to thank the department of neurosurgery, University Hospital Leipzig, for the clinical study and data collection in the context of an earlier research project funded by the German Research Society (Deutsche Forschungsgemeinschaft).

Funding

This work has been funded by the Mexican Council on Science and Technology (CONACyT) (Grant Number 493442).

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Authors and Affiliations

  1. CA Telematics, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, 36885, Salamanca, Mexico

    Elisee Ilunga-Mbuyamba, Juan Gabriel Avina-Cervantes & Jean Fulbert Ituna-Yudonago

  2. Department of Neurosurgery, University Hospital Leipzig, 04103, Leipzig, Germany

    Dirk Lindner & Felix Arlt

  3. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103, Leipzig, Germany

    Elisee Ilunga-Mbuyamba & Claire Chalopin

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  1. Elisee Ilunga-Mbuyamba
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Correspondence to Juan Gabriel Avina-Cervantes.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Lindner, D. et al. Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images. Int J CARS 13, 331–342 (2018). https://doi.org/10.1007/s11548-018-1703-0

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  • DOI: https://doi.org/10.1007/s11548-018-1703-0

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