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Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound.

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 ofte...

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Published in:Ultrasonic Imaging Vol. 38; no. 3; pp. 209 - 225
Main Authors: Pons, Gerard, Martí, Joan, Martí, Robert, Ganau, Sergi, Noble, J Alison
Format: research Journal Article
Published: Sage Publications Inc. May2016
Online Access:View this record in EBSCOhost
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      dt: May2016
      vid: 38
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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      tig:
        atl: Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound.
      aug:
        au:
          Pons, Gerard
          Martí, Joan
          Martí, Robert
          Ganau, Sergi
          Noble, J Alison
        affil: Department of Computer Architecture and Technology, University of Girona, Girona, Spain
      sug:
        subj:
          Ultrasonography
          Breast Neoplasms
          Image Processing, Computer Assisted Methods
          Models, Statistical
          Female
          Algorithms
          Human
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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