Τίτλος:
Automatic identification and localisation of potential malignancies in contrast-enhanced ultrasound liver scans using spatio-temporal features
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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 multicentre study, demonstrates the value of the proposed method. © Springer International Publishing Switzerland 2014.
Συγγραφείς:
Bakas, S.
Makris, D.
Sidhu, P.S.
Chatzimichail, K.
Περιοδικό:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Λέξεις-κλειδιά:
Automation; Computer aided diagnosis; Diagnosis; Liver; Ultrasonics, Clustering; Contrast enhanced ultrasound; Focal liver lesions; Localisation; Perfusion, Medical imaging
DOI:
10.1007/978-3-319-13692-9_2
