Abstract
Pre-operative locoregional treatments (PLT) delay the tumor progression by necrosis for patients with hepato-cellular carcinoma (HCC). Toward an efficient evaluation of PLT response, we address the estimation of liver tumor necrosis (TN) from CT scans. The TN rate could shortly supplant standard criteria (RECIST, mRECIST, EASL or WHO) since it has recently shown higher correlation to survival rates. To overcome the inter-expert variability induced by visual qualitative assessment, we propose a semi-automatic method that requires weak interaction efforts to segment parenchyma, tumoral active and necrotic tissues. By combining SLIC supervoxels and random decision forest, it involves discriminative multi-phase cluster-wise features extracted from registered dynamic contrast-enhanced CT scans. Quantitative assessment on expert groundtruth annotations confirms the benefits of exploiting multi-phase information from semantic regions to accurately segment HCC liver tumors.
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Ronot, M., Bouattour, M., Wassermann, J., Bruno, O., Dreyer, C., Larroque, B., Castera, L., Vilgrain, V., Belghiti, J., Raymond, E., et al.: Alternative response criteria (Choi, EASL and mRECIST) versus RECIST 1.1 in patients with advanced hepatocellular carcinoma treated with Sorafenib. The Oncologist (2014)
Ronot, M., Vilgrain, V.: Hepatocellular carcinoma: Diagnostic criteria by imaging techniques. Best Practice & Research Clinical Gastro-enterology 28(5) (2014)
Lee, J., Cai, W., Singh, A., Yoshida, H.: Estimation of necrosis volumes in focal liver lesions based on multi-phase hepatic CT images. In: Virtual Colonoscopy & Abdominal Imaging. Computational Challenges & Clinical Opportunities (2011)
Raj, A., Juluru, K.: Visualization and segmentation of liver tumors using dynamic contrast MRI. In: Conference of Engineering in Medicine and Biology (2009)
Fang, R., Zabih, R., Raj, A., Chen, T.: Segmentation of liver tumor using efficient global optimal tree metrics graph cuts. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging. Computational and Clinical Applications. LNCS, vol. 7029, pp. 51–59. Springer, Heidelberg (2012)
Shimizu, A., Narihira, T., Furukawa, D., Kobatake, H., Nawano, S., Shinozaki, K.: Ensemble segmentation using Adaboost with application to liver lesion extraction from a CT volume. In: Workshop on 3D Segmentation in the Clinic (2008)
Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for ms lesion segmentation in multi-channel mr images. In: Medical Image Computing and Computer-Assisted Intervention (2010)
Breiman, L.: Random Forests. Machine learning 45(1), 5–32 (2001)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision 7(2–3) (2012)
Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT Images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 66–74. Springer, Heidelberg (2012)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Beg, M., Miller, M., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision (2005)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11), 2274–2282 (2012)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23(7), 903–921 (2004)
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Conze, PH., Rousseau, F., Noblet, V., Heitz, F., Memeo, R., Pessaux, P. (2015). Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_26
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DOI: https://doi.org/10.1007/978-3-319-24888-2_26
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