Autonomous robotic palpation: Machine learning techniques to identify hard inclusions in soft tissues

Localizing tumors and measuring tissue mechanical properties can be useful for surgical planning and evaluating progression of disease. In this paper, supervised machine learning algorithms enable mechanical localization of stiff inclusions in artificial tissue after autonomous robotic palpation. Elastography is used to generate training data for the learning algorithms, providing a non-invasive, inclusion-specific characterization of tissue biomechanics. In particular, elastography was used to characterize the stiffness of artificial tissue with an embedded hard inclusion. Once the inclusion was identified on the elastographic image, machine learning methods identified the difference in stiffness between the inclusion and surrounding soft tissue and generated classifiers, which were used to label stiffness values as either part of the inclusion or soft tissue. Next, data acquired via autonomous robotic palpation of the artificial tissue created a map of the stiffness distributed over the surface of the tissue. The points in this map were thresholded against the classifiers trained by the machine learning algorithms, and points theorized to belong to the hard inclusion were labeled. Centroid approximations of the hard inclusion based on this labeling show that classifying stiffness data acquired by autonomous robotic palpation and labeled by a classifier trained from elastography data provides a more accurate method of localizing hard inclusions than using unclassified data.