Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets. / Perslev, Mathias; Pai, Akshay; Runhaar, Jos; Igel, Christian; Dam, Erik B.

In: Journal of Magnetic Resonance Imaging, Vol. 55, No. 2, 2022, p. 1650-1663.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Perslev, M, Pai, A, Runhaar, J, Igel, C & Dam, EB 2022, 'Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets', Journal of Magnetic Resonance Imaging, vol. 55, no. 2, pp. 1650-1663. https://doi.org/10.1002/jmri.27978

APA

Perslev, M., Pai, A., Runhaar, J., Igel, C., & Dam, E. B. (2022). Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets. Journal of Magnetic Resonance Imaging, 55(2), 1650-1663. https://doi.org/10.1002/jmri.27978

Vancouver

Perslev M, Pai A, Runhaar J, Igel C, Dam EB. Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets. Journal of Magnetic Resonance Imaging. 2022;55(2):1650-1663. https://doi.org/10.1002/jmri.27978

Author

Perslev, Mathias ; Pai, Akshay ; Runhaar, Jos ; Igel, Christian ; Dam, Erik B. / Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets. In: Journal of Magnetic Resonance Imaging. 2022 ; Vol. 55, No. 2. pp. 1650-1663.

Bibtex

@article{e94a66dee77e440898ea458bea7f1242,
title = "Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets",
abstract = "BackgroundSegmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.PurposeTo evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.",
author = "Mathias Perslev and Akshay Pai and Jos Runhaar and Christian Igel and Dam, {Erik B.}",
year = "2022",
doi = "10.1002/jmri.27978",
language = "English",
volume = "55",
pages = "1650--1663",
journal = "Journal of Magnetic Resonance Imaging",
issn = "1053-1807",
publisher = "JohnWiley & Sons, Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets

AU - Perslev, Mathias

AU - Pai, Akshay

AU - Runhaar, Jos

AU - Igel, Christian

AU - Dam, Erik B.

PY - 2022

Y1 - 2022

N2 - BackgroundSegmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.PurposeTo evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.

AB - BackgroundSegmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.PurposeTo evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.

UR - https://doi.org/10.1002/jmri.27978

U2 - 10.1002/jmri.27978

DO - 10.1002/jmri.27978

M3 - Journal article

C2 - 34918423

VL - 55

SP - 1650

EP - 1663

JO - Journal of Magnetic Resonance Imaging

JF - Journal of Magnetic Resonance Imaging

SN - 1053-1807

IS - 2

ER -

ID: 287687378