Abstract
Automatic segmentation is an essential but challenging step for extracting quantitative imaging bio-markers for characterizing head and neck tumor in tumor detection, diagnosis, prognosis, treatment planning and assessment. The HEad and neCK TumOR Segmentation Challenge 2021 (HECKTOR 2021) provides a common platform for the following three tasks: 1) the automatic segmentation of the primary gross target volume (GTV) in the oropharynx region on FDG-PET and CT images; 2) the prediction of patient outcomes, namely Progression Free Survival (PFS) from the FDG-PET/CT images with automatic segmentation results and the available clinical data; and 3) the prediction of PFS with ground truth annotations. We participated in the first two tasks by further evaluating a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we proposed a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Radiomic features were extracted from the segmented tumors and used for PFS prediction. Our segmentation framework was trained using the 224 challenge training cases provided by HECKTOR 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.7693 with cross validation. By testing on the 101 testing cases, our model achieved an average DSC of 0.7608 and \(95\%\) Hausdorff distance of 3.27 mm. The overall PFS prediction yielded a concordance index (c-index) of 0.53 on the testing dataset (id: deepX).
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References
Parkin, M., et al.: Global cancer statistics. CA Cancer. J. Clin. 55(2), 74–108 (2005)
Bonner, J., et al.: Radiotherapy plus Cetuximab for localregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomized trial, and relation between Cetuximab-induced rash and survival. Lacent Oncol. 11(1), 21–28 (2010)
Chajon, E., et al.: Salivary gland-sparing other than parotid-sparing in definitive head-and-neck intensity-modulated radiotherapy dose not seem to jeopardize local control. Radiat. Oncol. 8(1), 132 (2013)
Gudi, S., et al.: Interobserver variability in the delineation of gross tumor 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 Radiat. Sci. 48(2), 184–192 (2017)
Andrearczyk, V., et al.: Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. Proc. MIDL 1–11, 2020 (2020)
Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 1–37. Springer, Cham (2022)
Oreiller, V., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Med. Image Anal. 77, 102336 (2021)
Vallieres, M., et al.: Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci. Rep. 7(1), 10117 (2017)
Andrearczyk, V., et al. Oropharynx detection in PET-CT for tumor segmentation. In: Irish Machine Vision and Image Processing (2020)
Moe, Y.M., et al.: Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers. In: Proceedings of MIDL (2019)
Zhao, X., et al.: Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys. Med. Biol. 64, 015011 (2019)
Zhong, Z., et al.: Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med. Phys. 46(2), 619–633 (2019)
Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hu, J., et al.: Squeeze-and-excitation networks. In: Proceedings of CVPR 2018, pp. 7132–7141 (2018)
Li, X., et al.: Selective kernel networks. In: Proceedings of CVPR 2019, pp. 510–519 (2019)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of CVPR 2016, pp. 770–778 (2016)
Yuan, Y., et al.: Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans. Med. Imaging 36(9), 1876–1886 (2017)
Yuan, Y. Hierachical convolutional-deconvolutional neural networks for automatic liver and tumor segmentation. arXiv preprint arXiv:1710.04540 (2017)
Yuan, Y.: Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv preprint arXiv:1703.05154 (2017)
Yuan, Y., et al.: Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks. IEEE J. Biomed. Health Informat. 23(2), 519–526 (2019)
Wu, Y., He, K.: Group Normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_1
Griethuysen, J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017)
Yuan, Y.: Automatic head and neck tumor segmentation in PET/CT with scale attention network. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds.) HECKTOR 2020. LNCS, vol. 12603, pp. 44–52. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67194-5_5
Yuan, Y.: Automatic brain tumor segmentation with scale attention network. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 285–294. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_26
Zhou, Z., et al.: UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020)
Acknowledgment
This work is supported by a research grant from Varian Medical Systems (Palo Alto, CA, USA), UL1TR001433 from the National Center for Advancing Translational Sciences, and R21EB030209 from the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health, National Institutes of Health, USA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Yuan, Y., Adabi, S., Wang, X. (2022). Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_17
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DOI: https://doi.org/10.1007/978-3-030-98253-9_17
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