Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation

References

• Grégoire V, Grau C, Lapeyre M, et al. Target volume selection and delineation (t and n) for primary radiation treatment of oral cavity, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinoma. Oral Oncol. 2018;87:131–137.
   PubMed Web of Science ®Google Scholar
• Vinod SK, Jameson MG, Min M, et al. Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies. Radiother Oncol. 2016;121(2):169–179.
   PubMed Web of Science ®Google Scholar
• Sanguineti G, Foppiano F, Marcenaro M, et al. On the delineation of the gross tumor volume and clinical target volume for head and neck squamous cell carcinomas. Tumori. 2001;87(3):152–161.
   Web of Science ®Google Scholar
• Grégoire V, Evans M, Le QT, et al. Delineation of the primary tumour clinical target volumes (ctv-p) in laryngeal, hypopharyngeal, oropharyngeal and oral cavity squamous cell carcinoma: Airo, caca, dahanca, eortc, georcc, gortec, hknpcsg, hncig, iag-kht, lprhht, ncic ctg, ncri, nrg oncology, phns, sbrt, somera, sro, sshno, trog consensus guidelines. Radiother Oncol. 2018;126(1):3–24.
   PubMed Web of Science ®Google Scholar
• Bondue C, Racadot S, Coutte A, et al. Volumetric and dosimetric comparison of two delineation guidelines for the radiation treatment of laryngeal squamous cell carcinoma. Clin Transl Radiat Oncol. 2019;19:1–11.
   PubMed Web of Science ®Google Scholar
• Bashkatov A, Hossain S, Yang X, et al. Publications repository-helmholtz-zentrum dresden-rossendorf. Radiother Oncol Phys Rev Lett. 2019;123(Sup 1):214503.
   PubMed Web of Science ®Google Scholar
• Vinod SK, Min M, Jameson MG, et al. A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology. J Med Imaging Radiat Oncol. 2016;60(3):393–406.
   PubMed Web of Science ®Google Scholar
• Jensen K, Friborg J, Hansen CR, et al. The danish head and neck cancer group (dahanca) 2020 radiotherapy guidelines. Radiother Oncol. 2020;151:149–151.
   PubMed Web of Science ®Google Scholar
• Isensee F, Jaeger PF, Kohl SA, et al. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203–211.
   PubMed Web of Science ®Google Scholar
• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431–3440.
   Google Scholar
• Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV); 2016. p. 565–571.
   Google Scholar
• Li X, Chen H, Qi X, et al. H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans Med Imaging. 2018;37(12):2663–2674.
   PubMed Web of Science ®Google Scholar
• Simpson AL, Antonelli M, Bakas S, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. CoRR. 2019;abs/1902.09063. Available from: http://arxiv.org/abs/1902.09063.
   Google Scholar
• Andrearczyk V, Oreiller V, Jreige M, et al. Overview of the hecktor challenge at miccai 2020: automatic head and neck tumor segmentation in pet/ct. In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge; Springer; 2020. p. 1–21.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention; Springer; 2015. p. 234–241.
   Google Scholar
• Çiçek Ö, Abdulkadir A, Lienkamp SS, et al. 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention; Springer; 2016. p. 424–432.
   Google Scholar
• Moe YM, Groendahl AR, Tomic O, et al. Deep learning-based auto-delineation of gross tumour volumes and involved nodes in pet/ct images of head and neck cancer patients. Eur J Nucl Med Mol Imaging. 2021. DOI:https://doi.org/10.1007/s00259-020-05125-x
   PubMed Web of Science ®Google Scholar
• Andrearczyk V, Oreiller V, Vallières M, et al. Automatic segmentation of head and neck tumors and nodal metastases in pet-CT scans. In: Medical imaging with deep learning; 2020.
   Google Scholar
• Clark K, Vendt B, Smith K, et al. The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–1057.
   PubMed Web of Science ®Google Scholar
• Li Q, Xu Y, Chen Z, et al. Tumor segmentation in contrast-enhanced magnetic resonance imaging for nasopharyngeal carcinoma: deep learning with convolutional neural network. Biomed Res Int. 2018;2018:9128527.
   PubMed Web of Science ®Google Scholar
• Yu L, Yang X, Chen H, et al. Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images. In: Thirty-first AAAI conference on artificial intelligence; 2017.
   Google Scholar
• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–778.
   Google Scholar
• Zhu W, Huang Y, Zeng L, et al. Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys. 2019;46(2):576–589.
   PubMed Web of Science ®Google Scholar
• Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 2980–2988.
   Google Scholar
• Guo Z, Guo N, Gong K, et al. Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network. Phys Med Biol. 2019;64(20):205015.
   PubMed Web of Science ®Google Scholar
• Klein S, Staring M, Murphy K, et al. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29(1):196–205.
   PubMed Web of Science ®Google Scholar
• Fortunati V, Verhaart RF, Angeloni F, et al. Feasibility of multimodal deformable registration for head and neck tumor treatment planning. Int J Radiat Oncol Biol Phys. 2014;90(1):85–93.
   PubMed Web of Science ®Google Scholar
• Fort S, Hu H, Lakshminarayanan B. Deep ensembles: a loss landscape perspective. arXiv preprint. 2020. abs/1912.02757. Available from: https://arxiv.org/abs/1912.02757.
   Google Scholar
• Gudi S, Ghosh-Laskar S, Agarwal JP, et al. Interobserver variability in the delineation of gross tumour volume and specified organs-at-risk during imrt for head and neck cancers and the impact of fdg-pet/ct on such variability at the primary site. J Med Imaging Radiation Sci. 2017;48(2):184–192.
   PubMed Web of Science ®Google Scholar
• Apolle R, Appold S, Bijl HP, et al. Inter-observer variability in target delineation increases during adaptive treatment of head-and-neck and lung cancer. Acta Oncol. 2019;58(10):1378–1385.
   PubMed Web of Science ®Google Scholar
• Carter KR, Kotlyarov E. Common causes of false positive f18 fdg pet/ct scans in oncology. Braz Arch Biol Technol. 2007;50(spe):29–35.
   Web of Science ®Google Scholar
• Kresnik E, Mikosch P, Gallowitsch H, et al. Evaluation of head and neck cancer with 18 f-fdg pet: a comparison with conventional methods. Eur J Nucl Med. 2001;28(7):816–821.
   Google Scholar
• Kendall A, Gal Y. What uncertainties do we need in Bayesian deep learning for computer vision? In: NIPS; 2017.
   Google Scholar
• Lakshminarayanan B, Pritzel A, Blundell C. Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint. 2016. abs/1612.01474. Available from: https://arxiv.org/abs/1612.01474.
   Google Scholar
• Isensee F, Petersen J, Klein A, et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation. CoRR. 2018. abs/1809.10486. Available from: http://arxiv.org/abs/1809.10486.
   Google Scholar
• Wang Y, Zhao L, Wang M, et al. Organ at risk segmentation in head and neck ct images using a two-stage segmentation framework based on 3d u-net. IEEE Access. 2019;7:144591–144602.
   Web of Science ®Google Scholar
• Wang C, MacGillivray T, Macnaught G, et al. A two-stage u-net model for 3d multi-class segmentation on full-resolution cardiac data. In: International Workshop on Statistical Atlases and Computational Models of the Heart; Springer; 2018. p. 191–199.
   Google Scholar