Abstract
Three 2D CNN (Convolutional Neuronal Networks) models, one for each patient-plane (axial, sagittal and coronal) plus two 3D CNN models were ensemble using two 3D models for Head and Neck tumor segmentation in CT and FDG-PET images. A Progression Free Survival (PFS) prediction, based on a Gaussian Process Regression (GPR) model was design on Matlab. Radiomic features such as Haralick textures, geometrical and statistical data were extracted from the automatic segmentation. A 35-feature selection process was performed over 1000 different features. An anti-concordant prediction was made based on the Kaplan-Meier estimator.
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References
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)
Valentin, O., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. In: Medical Image Analysis (2021) (under revision)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. arXiv 2016, arXiv:1606.04797
Razvan, P., Tomas, M., Yoshua, B.: On the difficulty of training Recurrent Neural Networks. arXiv: 2013, arXiv:1211.5063
Iglovikov, V., Shvets, A.: TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation. arXiv 2018, arXiv:1801.05746
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248−255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Mingxing, T., Quoc, V.L.: EfficientNet: Rethinking model scaling for convolutional neural networks. In: Kamalika, C., Ruslan, S., (eds.) Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, 09–15 Jun 2019, pp. 6105–6114. PMLR, Long Beach (2019)
Alexander, B., Alex, P., Eugene, K., Vladimir, I.I., Alexandr, A.K.: Albumentations: fast and flexible image augmentations. arXiv: 2018, arXiv:1809.06839
Davood, K., Septimiu, E.S.: Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks. arXiv 2019, arXiv:1904.10030
Diederik, P.K., Jimmy, L.B.: Adam: A Method for Stochastic Optimization. arXiv 2014, arXiv:1412.6980
Çiçek, Ö., et al.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. https://arxiv.org/abs/1606.06650 (2016)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(04), 834–848 (2018). https://doi.org/10.1109/TPAMI.2017.2699184
Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel "squeeze and excitation" blocks. IEEE Trans. Med. Imaging 38(2), 540–549 (2019). https://doi.org/10.1109/tmi.2018.2867261. PMID: 30716024
Boundary loss for highly unbalanced segmentation. https://arxiv.org/pdf/1812.07032.pdf
Hermans, J., Spanakis, G., Möckel, R.: Accumulated Gradient Normalization (2017)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Statist. Assn. 53, 457–481 (1958)
Swanson, K.R., Rostomily, R.C., Alvord, E.C., Jr.: A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br. J. Cancer 98, 113–119 (2008)
Swanson, K.R., Bridge, C., Murray, J.D., Ellsworth, C., Alvord, E.C., Jr.: Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J. Neurol. Sci. 216, 1–10 (2003)
Haralick, R., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Carl, P., Daniel, L.: Cooc3d (2008). https://www.mathworks.com/matlabcentral/fileexchange/19058-cooc3d. MATLAB Central File Exchange. Accessed 9 Mar 2020
Lewandowski, Z., Beyenal, H.: Fundamentals of Biofilm Research. CRC Press, Florida (2007)
Puttanawarut, C., Sirirutbunkajorn, N., Khachonkham, S., Pattaranutaporn, P., Wongsawat, Y.: Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients. Radiat. Oncol. 16(1), 220 (2021). https://doi.org/10.1186/s13014-021-01950-y. PMID: 34775975; PMCID: PMC8591796
Murakami, Y., et al.: Dose-based radiomic analysis (dosiomics) for intensity-modulated radiotherapy in patients with prostate cancer: Correlation between planned dose distribution and biochemical failure. Int. J. Radiat. Oncol. Biol. Phys. S0360–3016(21), 02627–02634 (2021). https://doi.org/10.1016/j.ijrobp.2021.07.1714. Epub ahead of print. PMID: 34706278
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Martinez-Larraz, A., Martí Asenjo, J., Álvarez Rodríguez, B. (2022). PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques. 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_16
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