Detection and segmentation of pathological structures by the extended graph-shifts algorithm.
We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.
Center for Computational Biology, Laboratory of Neuro Imaging, University of California, Los Angeles, USA. jcorso@ucla.edu
Jason J
JJ
Corso
Alan
A
Yuille
Nancy L
NL
Sicotte
Arthur
A
Toga
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity