Automatic Identification and Localisation of Potential Malignancies in Contrast-Enhanced Ultrasound Liver Scans Using Spatio-Temporal Features

Conference paper

DOI: 10.1007/978-3-319-13692-9_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8676)
Cite this paper as:
Bakas S., Makris D., Sidhu P.S., Chatzimichail K. (2014) Automatic Identification and Localisation of Potential Malignancies in Contrast-Enhanced Ultrasound Liver Scans Using Spatio-Temporal Features. In: Yoshida H., Näppi J., Saini S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science, vol 8676. Springer, Cham

Abstract

The identification and localisation of a focal liver lesion (FLL) in Contrast-Enhanced Ultrasound (CEUS) video sequences is crucial for liver cancer diagnosis, treatment planning and follow-up management. Currently, localisation and classification of FLLs between benign and malignant cases in CEUS are routinely performed manually by radiologists, in order to proceed with making a diagnosis, leading to subjective results, prone to misinterpretation and human error. This paper describes a methodology to assist clinicians who regularly perform these tasks, by discharging benign FLL cases and localise potential malignancies in a fully automatic manner by exploiting the perfusion dynamics of a CEUS video. The proposed framework uses local variations of intensity to distinguish between hyper- and hypo-enhancing regions and then analyse their spatial configuration to identify potentially malignant cases. Automatic localisation of the potential malignancy on the image plane is then addressed by clustering, using Expectation-Maximisation for Gaussian Mixture Models. A novel feature that combines description of local dynamic behaviour with spatial proximity is used in this process. Quantitative evaluation, on real clinical data from a retrospective multi-centre study, demonstrates the value of the proposed method.

Keywords

Localisation Malignancy identification Contrast-enhanced ultrasound Focal liver lesion Liver Perfusion Clustering 

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Digital Imaging Research Centre, Faculty of Science, Engineering and ComputingKingston UniversityLondonUK
  2. 2.Department of RadiologyKing’s College HospitalLondonUK
  3. 3.Evgenidion HospitalNational and Kapodistrian UniversityAthensGreece

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