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000152998 0247_ $$2pmc$$apmc:PMC6752289
000152998 0247_ $$2ISSN$$a2379-1381
000152998 0247_ $$2ISSN$$a2379-139X
000152998 037__ $$aDKFZ-2020-00095
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000152998 1001_ $$0P:(DE-HGF)0$$aBielak, Lars$$b0
000152998 245__ $$aAutomatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.
000152998 260__ $$aAnn Arbor, Michigan$$bGrapho Publications$$c2019
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000152998 520__ $$aPrecise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (ktrans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.
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000152998 7001_ $$0P:(DE-HGF)0$$aWiedenmann, Nicole$$b1
000152998 7001_ $$0P:(DE-He78)8d52e7ff1ccaac7dbf0232fdcb0168bd$$aNicolay, Nils$$b2
000152998 7001_ $$aLottner, Thomas$$b3
000152998 7001_ $$aFischer, Johannes$$b4
000152998 7001_ $$0P:(DE-HGF)0$$aBunea, Hatice$$b5
000152998 7001_ $$aGrosu, Anca-Ligia$$b6
000152998 7001_ $$0P:(DE-HGF)0$$aBock, Michael$$b7
000152998 773__ $$0PERI:(DE-600)2857000-5$$a10.18383/j.tom.2019.00010$$gVol. 5, no. 3, p. 292 - 299$$n3$$p292 - 299$$tTomography$$v5$$x2379-139X$$y2019
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