Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models.

Journal: Ultrasound in medicine & biology
PMID: 39676003

Abstract

OBJECTIVE: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practice. Here, we present the first study aiming to automate brain sulci segmentation in pre-term infants using ultrasound images.

Authors

  • María Regalado
    Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain; Neonatal Brain Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain. Electronic address: mregaladobernabe@gmail.com.
  • Nuria Carreras
    Neonatal Brain Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain. Electronic address: nuria.carrerasb@sjd.es.
  • Christian Mata
    Neonatal Brain Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain; Universitat Politècnica de Barcelona, Barcelona, Spain. Electronic address: christian.mata@upc.edu.
  • Arnau Oliver
    Research institute of Computer Vision and Robotics, University of Girona, Spain.
  • Xavier Lladó
    Research institute of Computer Vision and Robotics, University of Girona, Spain.
  • Thais Agut
    Neonatal Brain Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain. Electronic address: thais.agut@sjd.es.

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