Kavli Affiliate: Jing Wang
| First 5 Authors: Weiguo Cao, Marc J. Pomeroy, Zhengrong Liang, Yongfeng Gao, Yongyi Shi
| Summary:
The elasticity of soft tissues has been widely considered as a characteristic
property to differentiate between healthy and vicious tissues and, therefore,
motivated several elasticity imaging modalities, such as Ultrasound
Elastography, Magnetic Resonance Elastography, and Optical Coherence
Elastography. This paper proposes an alternative approach of modeling the
elasticity using Computed Tomography (CT) imaging modality for model-based
feature extraction machine learning (ML) differentiation of lesions. The model
describes a dynamic non-rigid (or elastic) deformation in differential manifold
to mimic the soft tissues elasticity under wave fluctuation in vivo. Based on
the model, three local deformation invariants are constructed by two tensors
defined by the first and second order derivatives from the CT images and used
to generate elastic feature maps after normalization via a novel signal
suppression method. The model-based elastic image features are extracted from
the feature maps and fed to machine learning to perform lesion classifications.
Two pathologically proven image datasets of colon polyps (44 malignant and 43
benign) and lung nodules (46 malignant and 20 benign) were used to evaluate the
proposed model-based lesion classification. The outcomes of this modeling
approach reached the score of area under the curve of the receiver operating
characteristics of 94.2 % for the polyps and 87.4 % for the nodules, resulting
in an average gain of 5 % to 30 % over ten existing state-of-the-art lesion
classification methods. The gains by modeling tissue elasticity for ML
differentiation of lesions are striking, indicating the great potential of
exploring the modeling strategy to other tissue properties for ML
differentiation of lesions.
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