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Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings

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Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings. / Ilvesmäki, T.; Hakulinen, U.; Eskola, H.

EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag, 2018. p. 635-638 (IFMBE Proceedings; Vol. 65).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Ilvesmäki, T, Hakulinen, U & Eskola, H 2018, Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings. in EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. IFMBE Proceedings, vol. 65, Springer Verlag, pp. 635-638, Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), 1/01/00. https://doi.org/10.1007/978-981-10-5122-7_159

APA

Ilvesmäki, T., Hakulinen, U., & Eskola, H. (2018). Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings. In EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017 (pp. 635-638). (IFMBE Proceedings; Vol. 65). Springer Verlag. https://doi.org/10.1007/978-981-10-5122-7_159

Vancouver

Ilvesmäki T, Hakulinen U, Eskola H. Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings. In EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag. 2018. p. 635-638. (IFMBE Proceedings). https://doi.org/10.1007/978-981-10-5122-7_159

Author

Ilvesmäki, T. ; Hakulinen, U. ; Eskola, H. / Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings. EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag, 2018. pp. 635-638 (IFMBE Proceedings).

Bibtex - Download

@inproceedings{59aa34c2a8f54495be0b959ca4a1a87f,
title = "Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings",
abstract = "Diffusion tensor imaging (DTI) has become a relatively common MR imaging technique in only 10 years. DTI can provide important information of brain microstructure in vivo. Many quantitative DTI analysis methods utilize either region of interest (ROI) or voxel-wise whole-brain methods. ROI methods do not require potentially bias-inducing image data altering, e.g., resampling and smoothing, and are the preferred method in clinical settings. We present an automated pipeline for quantitative ROI analysis of brain DTI data. The pipeline includes pre-processing, registrations, and calculation of mean (and SD) DTI scalar values from the automated ROIs. In addition to atlas regions, the pipeline accepts freehand ROIs, as long as the frame of reference is also provided. By the uniquely designed pipeline, we ensure that the results can be retrospectively compared to previously conducted manual freehand ROI measurement results, if desired. We validated the feasibility of the pipeline by comparing manual freehand ROI measurement results from 40 subjects against the results obtained from automated ROIs. A single set of freehand ROIs (drawn similarly to the original freehand manual ROIs in the population) was input to the pipeline, and the resulting scalar values from the automated ROIs were compared to the manual freehand ROIs’ data. Adopting a limit for goodness of fit of z = ± 1.6 resulted in 94 {\%} success rate for the pipeline’s automated ROI registrations in the whole population. The pipeline can reduce the time taken in clinical ROI measurements.",
keywords = "Atlas, DTI, Image analysis, Pipeline, ROI",
author = "T. Ilvesm{\"a}ki and U. Hakulinen and H. Eskola",
note = "jufoid=58152",
year = "2018",
doi = "10.1007/978-981-10-5122-7_159",
language = "English",
isbn = "9789811051210",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "635--638",
booktitle = "EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017",
address = "Germany",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Automated pipeline for brain ROI analysis with results comparable to previous freehand measures in clinical settings

AU - Ilvesmäki, T.

AU - Hakulinen, U.

AU - Eskola, H.

N1 - jufoid=58152

PY - 2018

Y1 - 2018

N2 - Diffusion tensor imaging (DTI) has become a relatively common MR imaging technique in only 10 years. DTI can provide important information of brain microstructure in vivo. Many quantitative DTI analysis methods utilize either region of interest (ROI) or voxel-wise whole-brain methods. ROI methods do not require potentially bias-inducing image data altering, e.g., resampling and smoothing, and are the preferred method in clinical settings. We present an automated pipeline for quantitative ROI analysis of brain DTI data. The pipeline includes pre-processing, registrations, and calculation of mean (and SD) DTI scalar values from the automated ROIs. In addition to atlas regions, the pipeline accepts freehand ROIs, as long as the frame of reference is also provided. By the uniquely designed pipeline, we ensure that the results can be retrospectively compared to previously conducted manual freehand ROI measurement results, if desired. We validated the feasibility of the pipeline by comparing manual freehand ROI measurement results from 40 subjects against the results obtained from automated ROIs. A single set of freehand ROIs (drawn similarly to the original freehand manual ROIs in the population) was input to the pipeline, and the resulting scalar values from the automated ROIs were compared to the manual freehand ROIs’ data. Adopting a limit for goodness of fit of z = ± 1.6 resulted in 94 % success rate for the pipeline’s automated ROI registrations in the whole population. The pipeline can reduce the time taken in clinical ROI measurements.

AB - Diffusion tensor imaging (DTI) has become a relatively common MR imaging technique in only 10 years. DTI can provide important information of brain microstructure in vivo. Many quantitative DTI analysis methods utilize either region of interest (ROI) or voxel-wise whole-brain methods. ROI methods do not require potentially bias-inducing image data altering, e.g., resampling and smoothing, and are the preferred method in clinical settings. We present an automated pipeline for quantitative ROI analysis of brain DTI data. The pipeline includes pre-processing, registrations, and calculation of mean (and SD) DTI scalar values from the automated ROIs. In addition to atlas regions, the pipeline accepts freehand ROIs, as long as the frame of reference is also provided. By the uniquely designed pipeline, we ensure that the results can be retrospectively compared to previously conducted manual freehand ROI measurement results, if desired. We validated the feasibility of the pipeline by comparing manual freehand ROI measurement results from 40 subjects against the results obtained from automated ROIs. A single set of freehand ROIs (drawn similarly to the original freehand manual ROIs in the population) was input to the pipeline, and the resulting scalar values from the automated ROIs were compared to the manual freehand ROIs’ data. Adopting a limit for goodness of fit of z = ± 1.6 resulted in 94 % success rate for the pipeline’s automated ROI registrations in the whole population. The pipeline can reduce the time taken in clinical ROI measurements.

KW - Atlas

KW - DTI

KW - Image analysis

KW - Pipeline

KW - ROI

U2 - 10.1007/978-981-10-5122-7_159

DO - 10.1007/978-981-10-5122-7_159

M3 - Conference contribution

SN - 9789811051210

T3 - IFMBE Proceedings

SP - 635

EP - 638

BT - EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017

PB - Springer Verlag

ER -