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 artific...

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Bibliographic Details
Published inIEEE transactions on robotics Vol. 31; no. 2; pp. 344 - 354
Main Authors Nichols, Kirk A., Okamura, Allison M.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Artificial intelligence
Autonomous
Biological tissues
Classifiers
Discriminant analysis
Image segmentation
Inclusions
Learning and adaptive systems
Machine learning algorithms
Markov analysis
Mechanical properties
medical robots and systems
Position (location)
Robotics
Robots
Segmentation
Soft tissues
Surgery
Tumors
Learning and adaptive systems
medical robots and systems
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Summary: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.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2015.2402531