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Tomography
Volume 5
Issue 3
10.18383/j.tom.2019.00010
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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
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Article

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction

by
Lars Bielak
1,3,*,
Nicole Wiedenmann
2,3,
Nils Henrik Nicolay
2,3,
Thomas Lottner
1,
Johannes Fischer
1,
Hatice Bunea
2,3,
Anca-Ligia Grosu
2,3 and
Michael Bock
1,3
1
Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
2
Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
3
German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(3), 292-299; https://doi.org/10.18383/j.tom.2019.00010
Submission received: 6 June 2019 / Revised: 9 July 2019 / Accepted: 8 August 2019 / Published: 1 September 2019
Versions Notes

Abstract

Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (ktrans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.
Keywords: Multi-parametric MRI; radiation therapy; automatic tumor segmentation; convolutional neuronal network Multi-parametric MRI; radiation therapy; automatic tumor segmentation; convolutional neuronal network

Share and Cite

MDPI and ACS Style

Bielak, L.; Wiedenmann, N.; Nicolay, N.H.; Lottner, T.; Fischer, J.; Bunea, H.; Grosu, A.-L.; Bock, M. Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction. Tomography 2019, 5, 292-299. https://doi.org/10.18383/j.tom.2019.00010

AMA Style

Bielak L, Wiedenmann N, Nicolay NH, Lottner T, Fischer J, Bunea H, Grosu A-L, Bock M. Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction. Tomography. 2019; 5(3):292-299. https://doi.org/10.18383/j.tom.2019.00010

Chicago/Turabian Style

Bielak, Lars, Nicole Wiedenmann, Nils Henrik Nicolay, Thomas Lottner, Johannes Fischer, Hatice Bunea, Anca-Ligia Grosu, and Michael Bock. 2019. "Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction" Tomography 5, no. 3: 292-299. https://doi.org/10.18383/j.tom.2019.00010

APA Style

Bielak, L., Wiedenmann, N., Nicolay, N. H., Lottner, T., Fischer, J., Bunea, H., Grosu, A. -L., & Bock, M. (2019). Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction. Tomography, 5(3), 292-299. https://doi.org/10.18383/j.tom.2019.00010

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