Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation

• URL: http://arxiv.org/abs/2102.08556v1
• Date: Wed, 17 Feb 2021 03:52:02 GMT
• Title: Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation
• Authors: Jue Jiang, Sadegh Riyahi Alam, Ishita Chen, Perry Zhang, Andreas Rimner, Joseph O. Deasy, Harini Veeraraghavan
• Abstract summary: We developed a new deep learning CBCT lung tumor segmentation method. Key idea of our approach is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDN) using unpaired CBCT and MRI datasets.
• Score: 3.8791511769387634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. Methods: The key idea of our approach called cross modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training to extract more informative features during training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDN) using unpaired CBCT and MRI datasets. Feature distillation regularizes the student network to extract CBCT features that match the statistical distribution of MRI features extracted by the teacher network and obtain better differentiation of tumor from background.} We also compared against an alternative framework that used UDA with MR segmentation network, whereby segmentation was done on the synthesized pseudo MRI representation. All networks were trained with 216 weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different patient cohorts. Validation was done on 20 weekly CBCTs from patients not used in training. Independent testing was done on 38 weekly CBCTs from patients not used in training or validation. Segmentation accuracy was measured using surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th percentile (HD95) metrics.

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