A novel approach for Hepatocellular Carcinoma detection and classification based on triphasic CT Protocol

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation.

[1]  Vitoantonio Bevilacqua,et al.  On the Comparison of NN-Based Architectures for Diabetic Damage Detection in Retinal Images , 2009, J. Circuits Syst. Comput..

[2]  Vitoantonio Bevilacqua,et al.  An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy , 2017, Neurocomputing.

[3]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[4]  A. Burroughs,et al.  Hepatocellular carcinoma , 2003, The Lancet.

[5]  R. Lencioni,et al.  The etiology of hepatocellular carcinoma and consequences for treatment. , 2010, The oncologist.

[6]  Masatoshi Kudo,et al.  Multistep human hepatocarcinogenesis: correlation of imaging with pathology , 2009, Journal of Gastroenterology.

[7]  H. Edmondson,et al.  Primary carcinoma of the liver. A study of 100 cases among 48,900 necropsies , 1954, Cancer.

[8]  J. Bruix,et al.  Prognosis of Hepatocellular Carcinoma: The BCLC Staging Classification , 1999, Seminars in liver disease.

[9]  G Angelelli,et al.  Hereditary haemorrhagic telangiectasia: study of hepatic vascular alterations with multi-detector row helical CT and reconstruction programs. , 2005, La Radiologia medica.

[10]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[11]  Vitoantonio Bevilacqua,et al.  Atlas-Based Segmentation of Organs at Risk in Radiotherapy in Head MRIs by Means of a Novel Active Contour Framework , 2010, ICIC.

[12]  Tara L. Kieffer,et al.  Hepatocellular Carcinoma: Epidemiology and Molecular Carcinogenesis , 2009 .

[13]  Nor Ashidi Mat Isa,et al.  Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction , 2010, IEEE Signal Processing Letters.

[14]  Masatoshi Kudo,et al.  Validation of a new prognostic staging system for hepatocellular carcinoma: The JIS score compared with the CLIP score , 2004, Hepatology.

[15]  Vitoantonio Bevilacqua,et al.  An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification , 2016, GECCO.

[16]  Gianpaolo Francesco Trotta,et al.  Synthesis of a Neural Network Classifier for Hepatocellular Carcinoma Grading Based on Triphasic CT Images , 2016, RTIP2R.

[17]  Antonio Frisoli,et al.  Relive: a serious game to learn how to save lives. , 2014, Resuscitation.

[18]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[19]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[20]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[22]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[23]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[24]  J. Bruix,et al.  Current Strategy for Staging and Treatment: The BCLC Update and Future Prospects , 2010, Seminars in liver disease.

[25]  Raymond H. Chan,et al.  Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.

[26]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[27]  Vitoantonio Bevilacqua,et al.  Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective , 2007, 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

[28]  Vitoantonio Bevilacqua,et al.  A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[29]  J. Welch,et al.  Current Surgical Therapy 1984-1985 , 1985 .

[30]  Arnaldo Scardapane,et al.  Oral contrast-enhanced three-dimensional helical-CT cholangiography: clinical applications , 2003, European Radiology.

[31]  Carlo Bartolozzi,et al.  Biliary and reticuloendothelial impairment in hepatocarcinogenesis: the diagnostic role of tissue-specific MR contrast media , 2007, European Radiology.

[32]  J. Bruix,et al.  Management of hepatocellular carcinoma , 2005, Hepatology.

[33]  E. McFadden,et al.  Toxicity and response criteria of the Eastern Cooperative Oncology Group , 1982, American journal of clinical oncology.

[34]  Veerakumar Thangaraj,et al.  Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter , 2011, IEEE Signal Processing Letters.

[35]  S Bengmark,et al.  The natural history of primary and secondary malignant tumors of the liver I. The prognosis for patients with hepatic metastases from colonic and rectal carcinoma by laparotomy , 1969, Cancer.

[36]  C. Sirlin,et al.  CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part II. Extracellular agents, hepatobiliary agents, and ancillary imaging features. , 2014, Radiology.

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[38]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[39]  Xiaofeng Wang,et al.  Shape recognition based on neural networks trained by differential evolution algorithm , 2007, Neurocomputing.

[40]  L. Sobin,et al.  World Health Organization classification of tumors , 2000, Cancer.