Methods to Segment Hard Inclusions in Soft Tissue During Autonomous Robotic Palpation

Localizing tumors and measuring tissue mechanical properties can aid in surgical planning and evaluating the progression of disease. In this paper, autonomous robotic palpation with supervised machine learning algorithms enables mechanical localization and segmentation of stiff inclusions in artificial tissue. Elastography generates training data for the learning algorithms, providing a noninvasive, inclusion-specific characterization of tissue mechanics. Once an embedded hard inclusion was identified in the elastographic image, Gaussian discriminant analysis generated a classifier to threshold stiffness values acquired from autonomous robotic palpation. This classifier was later used to classify newly acquired points as either part of the inclusion or surrounding soft tissue. An expectation-maximization algorithm with underlying Markov random fields improved this initial classifier over successive iterations to better approximate the boundary of the inclusion. Results demonstrate robustness with respect to inclusion shape, size, and the initial classifier value. For three trials segmenting a cubic inclusion, sensitivity was above 0.95 and specificity was above 0.92.