title: Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.
abstract: Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance.
authors.affiliation: Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, New York 10065, United States; City College of New York, Department of Computer Science, 160 Convent Avenue, New York, New York 10031, United States.
authors.forename: Yu-Chi
authors.initials: YC
authors.lastname: Hu
authors.affiliation: City College of New York , Department of Computer Science, 160 Convent Avenue, New York, New York 10031, United States.
authors.forename: Michael
authors.initials: M
authors.lastname: Grossberg
authors.affiliation: Memorial Sloan Kettering Cancer Center , Department of Medical Physics, 1275 York Avenue, New York, New York 10065, United States.
authors.forename: Gikas
authors.initials: G
authors.lastname: Mageras
keywords: conditional random field; logistic regression; multimodality imaging; semiautomatic segmentation; tumor