Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms. / Cutter, Jennifer R; Styles, Iain; Leonardis, Ales; Dehghani, Hamid.

International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016. Elsevier, 2016. p. 28-34 (Procedia Computer Science; Vol. 90).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Cutter, JR, Styles, I, Leonardis, A & Dehghani, H 2016, Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms. in International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016. Procedia Computer Science, vol. 90, Elsevier, pp. 28-34, Medical Image Understanding and Analysis Conference (MIUA 2016), Loughborough, United Kingdom, 6/07/16. https://doi.org/10.1016/j.procs.2016.07.006

APA

Cutter, J. R., Styles, I., Leonardis, A., & Dehghani, H. (2016). Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms. In International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016 (pp. 28-34). (Procedia Computer Science; Vol. 90). Elsevier. https://doi.org/10.1016/j.procs.2016.07.006

Vancouver

Cutter JR, Styles I, Leonardis A, Dehghani H. Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms. In International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016. Elsevier. 2016. p. 28-34. (Procedia Computer Science). https://doi.org/10.1016/j.procs.2016.07.006

Author

Cutter, Jennifer R ; Styles, Iain ; Leonardis, Ales ; Dehghani, Hamid. / Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms. International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016. Elsevier, 2016. pp. 28-34 (Procedia Computer Science).

Bibtex

@inproceedings{db1f17d509bb47c392a58a2dc52a53e4,
title = "Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms",
abstract = "Stereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of pre-operative MRI/CT images with patient and robot coordinate systems during surgery. Fiducial markers or a stereotactic frame are used as registration landmarks; the patient{\textquoteright}s head is fixed in position throughout surgery. An image-based system could be quicker and less invasive, allowing the head to be moved during surgery to give greater ease of access, but would be required to retain a surgical precision of ~1mm at the target point. We compare two registration algorithms, iterative closest point (ICP) and coherent point drift (CPD), by registering ideal point clouds taken from MRI data with re-meshed, noisy and smoothed versions. We find that ICP generally gives better and more consistent registration accuracy for the region of interest than CPD, with a best RMS distance of 0.884±0.050 mm between aligned point clouds, as compared to 0.995±0.170 mm or worse for CPD.",
author = "Cutter, {Jennifer R} and Iain Styles and Ales Leonardis and Hamid Dehghani",
year = "2016",
doi = "10.1016/j.procs.2016.07.006",
language = "English",
series = "Procedia Computer Science",
publisher = "Elsevier",
pages = "28--34",
booktitle = "International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016",
note = "Medical Image Understanding and Analysis Conference (MIUA 2016) ; Conference date: 06-07-2016 Through 08-07-2016",

}

RIS

TY - GEN

T1 - Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms

AU - Cutter, Jennifer R

AU - Styles, Iain

AU - Leonardis, Ales

AU - Dehghani, Hamid

PY - 2016

Y1 - 2016

N2 - Stereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of pre-operative MRI/CT images with patient and robot coordinate systems during surgery. Fiducial markers or a stereotactic frame are used as registration landmarks; the patient’s head is fixed in position throughout surgery. An image-based system could be quicker and less invasive, allowing the head to be moved during surgery to give greater ease of access, but would be required to retain a surgical precision of ~1mm at the target point. We compare two registration algorithms, iterative closest point (ICP) and coherent point drift (CPD), by registering ideal point clouds taken from MRI data with re-meshed, noisy and smoothed versions. We find that ICP generally gives better and more consistent registration accuracy for the region of interest than CPD, with a best RMS distance of 0.884±0.050 mm between aligned point clouds, as compared to 0.995±0.170 mm or worse for CPD.

AB - Stereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of pre-operative MRI/CT images with patient and robot coordinate systems during surgery. Fiducial markers or a stereotactic frame are used as registration landmarks; the patient’s head is fixed in position throughout surgery. An image-based system could be quicker and less invasive, allowing the head to be moved during surgery to give greater ease of access, but would be required to retain a surgical precision of ~1mm at the target point. We compare two registration algorithms, iterative closest point (ICP) and coherent point drift (CPD), by registering ideal point clouds taken from MRI data with re-meshed, noisy and smoothed versions. We find that ICP generally gives better and more consistent registration accuracy for the region of interest than CPD, with a best RMS distance of 0.884±0.050 mm between aligned point clouds, as compared to 0.995±0.170 mm or worse for CPD.

U2 - 10.1016/j.procs.2016.07.006

DO - 10.1016/j.procs.2016.07.006

M3 - Conference contribution

T3 - Procedia Computer Science

SP - 28

EP - 34

BT - International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016

PB - Elsevier

T2 - Medical Image Understanding and Analysis Conference (MIUA 2016)

Y2 - 6 July 2016 through 8 July 2016

ER -