title: Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound.
abstract: Breast ultrasound (BUS) imaging has become a crucial modality, especially for providing a complementary view when other modalities (i.e., mammography) are not conclusive in the task of assessing lesions. The specificity in cancer detection using BUS imaging is low. These false-positive findings often lead to an increase of unnecessary biopsies. In addition, increasing sensitivity is also challenging given that the presence of artifacts in the B-mode ultrasound (US) images can interfere with lesion detection. To deal with these problems and improve diagnosis accuracy, ultrasound elastography was introduced. This paper validates a novel lesion segmentation framework that takes intensity (B-mode) and strain information into account using a Markov Random Field (MRF) and a Maximum a Posteriori (MAP) approach, by applying it to clinical data. A total of 33 images from two different hospitals are used, composed of 14 cancerous and 19 benign lesions. Results show that combining both the B-mode and strain data in a unique framework improves segmentation results for cancerous lesions (Dice Similarity Coefficient of 0.49 using B-mode, while including strain data reaches 0.70), which are difficult images where the lesions appear with blurred and not well-defined boundaries.
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Girona, Spain gpons@eia.udg.edu.
authors.forename: Gerard
authors.initials: G
authors.lastname: Pons
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Girona, Spain.
authors.forename: Joan
authors.initials: J
authors.lastname: Martí
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Girona, Spain.
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
authors.affiliation: Radiology Department, UDIAT-Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain.
authors.forename: Sergi
authors.initials: S
authors.lastname: Ganau
authors.affiliation: Department of Engineering Science, Institute of Biomedical Engineering, Old Road Campus Research Building, University of Oxford, Oxford, UK.
authors.forename: J Alison
authors.initials: JA
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D054459:Elasticity Imaging Techniques; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D016217:Ultrasonography, Mammary
keywords: Markov Random Fields; breast cancer; elastography; lesion segmentation; strain; ultrasound