Confidence-Based Segmentation of MR Imagery Using Region and Boundary Information with Nonlinear Scale-Space and Fast Marching Level Sets

Automatic segmentation of stroke lesions in magnetic resonance imagery is a difficult problem because anatomical knowledge is required for the most accurate decisions. Without such knowledge, classification rules seem inconsistent. We propose a hybrid boundary and region based segmentation model built upon nonlinear scalespace and geometric active contours that captures the various segmentation rules necessary to segment lesions. After a user selects a point within damaged tissue and another point within healthy tissue, the image is examined at several levels of detail. At each such scale, the lesion is segmented several times by varying a parameter that models the range of criteria for boundaries between healthy and damaged tissue. These segmentations are collected, and the relative frequency of tissue being labeled lesion is regarded as a measure of confidence in the classification of the tissue as damaged. Experiments compare volumes and segmentations of lesions given by physicians to those given by the automatic method. Performance upper bounds are established by matching automatic segmentation parameters (scale, threshold, and/or confidence) for each image with each physician’s hand segmentation. These results may be compared with results that fix parameters for a particular physician’s segmentation or all physicians’ segmentations. Sensitivity to parameter values and initializations are tested as well. With little initialization, the model achieves zero error on average with a standard deviation near clinically useful bounds. A modest amount of additional input gives zero error on each image.