Ultrasound B-mode image segmentation and radiomics

Topic:

Ultrasound imaging is widely used in medicine thanks to its many advantages: this versatile imaging technique does not use ionizing radiation, is inexpensive, allows real-time imaging, and is portable, allowing for crucial point-of-care applications. On the downside, it is highly dependent on the operator and requires skilled trained professionals for acquiring the image and interpreting it. Computer-aided-diagnosis systems can mitigate part of this drawback by providing automatic solutions for segmenting areas of interest and for computing measurements and radiomic features which aid the final diagnosis.

Beginning as pioneers for the completely automatic measurement of the intima-media thickness and plaque characterization in carotid artery ultrasound images for cardiovascular risk assessment, this line of research has been further extended to numerous other applications. In particular, it now includes:

  • Musculoskeletal ultrasonography for texture analysis and for the automatic segmentation of muscles in longitudinal and transversal images using traditional and deep learning techniques. The automatic segmentation is employed for the subsequent automatic measurement of muscle thickness (longitudinal images) and the cross-sectional area and mean gray scale value among other texture features (transversal images), which are key in distinguishing normal from pathological muscle images.
  • Transorbital ultrasonography for the automatic segmentation of the optic nerve and the consequent measurement of the optic nerve diameter and optic nerve sheath diameter, which are helpful for the estimation and monitoring of increased intra-cranial pressure.
  • Testicular ultrasonography for radiomics analysis and in particular for measuring gonadal function. First-order and higher-order texture features are used to correlate B-mode image features with gonadal functions and semen parameters, capable of representing a mirror of the pituitary-gonadal homeostasis in terms of reproductive function.

People:

  • Filippo Molinari (Full Professor)
  • Kristen M. Meiburger (Tenure-track Assistant Professor (RTD/b))
  • Francesco Marzola (Ph.D. Student)

    • Collaborators:

  • Prof. U. Rajendra Acharya - Ngee Ann Polytechnic, Singapore
  • Prof. Marco Minetto - Università degli Studi di Torino
  • Prof. Nens van Alfen, Jonne Doorduin - Radboud University Medical Centre
  • Dr. Piergiorgio Lochner - Saarland University Medical Center

    • Recent publications:

  • Carotid Ultrasound Boundary Study (CUBS): An Open Multicenter Analysis of Computerized Intima–Media Thickness Measurement Systems and Their Clinical Impact
      Kristen M.Meiburger, Guillaume Zahnd, Francesco Faita, Christos P. Loizou, Catarina Carvalho, David A. Steinman, Lorenzo Gibello, Rosa Maria Bruno, Francesco Marzola, Ricarda Clarenbach, Martina Francesconi, Andrew N. Nicolaides, Aurelio Campilho, Reza Gh
      Ultrasound in Medicine and Biology
      10.1016/j.ultrasmedbio.2021.03.022
  • Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans
      K. M. Meiburger, F. Marzola, G. Zahnd, F. Faita, C. P. Loizou, N. Lainé, C. Carvalho, D. A. Steinman, L. Gibello, R.M. Bruno, R. Clarenbach, M. Francesconi, A. N. Nicolaides, H. Liebgott, A. Campilho, R. Ghotbi, E. Kyriacou, N. Navab, M. Griffin, A. G. Pa
      Computers in Biology and Medicine
      10.1016/j.compbiomed.2022.105333
  • Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images
      Kristen M. Meiburger, Andrea Naldi, Nicola Michielli, Lorenzo Coppo,, Klaus Fassbender, Filippo Molinari, Piergiorgio Lochner
      Ultrasound in Medicine & Biology
      10.1016/j.ultrasmedbio.2020.01.034
  • Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks
      Francesco Marzola; Nens van Alfen; Massimo Salvi; Bruno De Santi; Jonne Doorduin; Kristen M. Meiburger
      2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
      10.1109/EMBC44109.2020.9176343