A fully automated image analysis framework for quantitative assessment of temporal tumor changes

This paper presents a novel framework for assessing tumor changes based on histogram analysis of temporal Magnetic Resonance Image (MRI) data. The proposed method detects the distribution of tumor and quantitatively models its growth or shrinkage offering the potential to assist clinicians in objectively assessing subtle changes during therapy. The presented work and the initial validation refer to the glioma case but can be generalized to any type of cancer where medical imaging is routinely used to characterize tumor response over time.

[1]  S. Dellepiane,et al.  Image Segmentation: Errors, sensitivity, and uncertainty , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[2]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[3]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[4]  Stephen T. C. Wong,et al.  Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field , 2009, Comput. Medical Imaging Graph..

[5]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[6]  Mark W. Schmidt,et al.  Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines , 2005, CVBIA.

[7]  T. Cascino,et al.  Response criteria for phase II studies of supratentorial malignant glioma. , 1990, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[9]  H. Benali,et al.  BrainVISA: Software platform for visualization and analysis of multi-modality brain data , 2001, NeuroImage.

[10]  A. Miller,et al.  Reporting results of cancer treatment , 1981, Cancer.

[11]  Paul Sajda,et al.  Machine learning for detection and diagnosis of disease. , 2006, Annual review of biomedical engineering.

[12]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Kai-Kuang Ma,et al.  Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine , 2004 .

[14]  Guido Gerig,et al.  Automatic brain tumor segmentation by subject specific modification of atlas priors. , 2003, Academic radiology.

[15]  Alan L. Yuille,et al.  Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification , 2008, IEEE Transactions on Medical Imaging.

[16]  M. Okada,et al.  [New response evaluation criteria in solid tumours-revised RECIST guideline (version 1.1)]. , 2009, Gan to kagaku ryoho. Cancer & chemotherapy.

[17]  Alan L. Yuille,et al.  Automated MR image processing and analysis of malignant brain tumors: enabling technology for data mining , 2007 .

[18]  Jonathan G. Goldin,et al.  CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI , 2009, Algorithms.

[19]  Nixon,et al.  Feature Extraction & Image Processing , 2008 .

[20]  Dewey Odhner,et al.  A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[21]  David A. Rottenberg,et al.  Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm , 2007, NeuroImage.

[22]  Alan L. Yuille,et al.  Hierarchical segmentation of malignant gliomas via integrated contextual filter response , 2008, SPIE Medical Imaging.

[23]  M. Platten,et al.  Neuroradiologische Responsekriterien bei malignen Gliomen , 2010, Der Nervenarzt.

[24]  Nan Zhang,et al.  Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation , 2011, Comput. Vis. Image Underst..

[25]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[26]  Luc Soler,et al.  Liver Registration for the Follow-Up of Hepatic Tumors , 2005, MICCAI.