Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning. (July 2021)

• Record Type: Journal Article
• Authors: Rodríguez Outeiral, Roque; Bos, Paula; Al-Mamgani, Abrahim; Jasperse, Bas; Simões, Rita; van der Heide, Uulke A.
• Is Part Of: Physics and imaging in radiation oncology. Volume 19(2021)
• Page Start: 39
• Page End: 44
• Publication Date: 2021-07
• Subjects: Oropharyngeal cancer -- Convolutional neural network -- Segmentation -- MRI -- Radiotherapy -- Semi-automatic approach; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2021.06.005
• Languages: English
• ISSNs: 2405-6316

Abstract

Highlights: Reducing the context around the tumor results in better segmentations with CNNs. Combining multiple MRI sequences as input can be beneficial to segmentation networks. A semi-automatic approach is fast and can potentially improve the accuracy of segmentations. Abstract: Background and purpose: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic. Materials and methods: We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performance using different combinations of MRI sequences as input. Finally, a semi-automatic approach by two human observers defining clipboxes around the tumor was tested. Segmentation performance was measured with Sørensen–Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The 3D UNet trained with full context and all sequences as input yielded a median Dice of 0.55, HD of 8.7 mm and MSD of 2.7 mm. Combining all MRI sequences was better than using single sequences. The semi-automatic approach with all sequences as input yielded significantly better performance (p < 0.001): a median Dice of 0.74, HD of 4.6 mm and MSD of 1.2 mm. Conclusion: Reducing the amount of context around the tumor and combining multiple MRI sequences improved the segmentation performance. A semi-automatic approach was accurate and clinically feasible.