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Multiple Organs Segmentation in Abdomen CT Scans Using a Cascade of CNNs

  • Muhammad Usman AkbarEmail author
  • Shahab Aslani
  • Vitorio Murino
  • Diego Sona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11751)

Abstract

Automatic organ segmentation is a vital prerequisite of many clinical application in radiology. The anatomical variability of organs in the abdomen makes it difficult for many methods to obtain good segmentations for all organs. In this paper, we present a particular ensemble of convolutional neural networks, combining technologies that analyze the images with either a local or a global perspective. In particular, we implemented a cascade of models combining the advantages of using local and global processing. We have evaluated our proposed system on CT scan of 30 subjects in a nested cross-validation framework, showing a significant performance improvement if compared with state-of-the-art methods.

Keywords

Deep learning Ensemble learning Convolutional neural networks Medical imaging Segmentation Abdomen organs 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad Usman Akbar
    • 1
    • 2
    Email author
  • Shahab Aslani
    • 1
    • 2
  • Vitorio Murino
    • 1
    • 3
  • Diego Sona
    • 1
    • 4
  1. 1.Pattern Analysis and Computer VisionIstituto Italiano di TecnologiaGenovaItaly
  2. 2.Department of Electrical, Electronics and Telecommunication Engineering and Naval ArchitectureUniversità degli Studi di GenovaGenoaItaly
  3. 3.Department of Computer ScienceUniversità di VeronaVeronaItaly
  4. 4.Neuroinformatics LaboratoryFondazione Bruno KesslerTrentoItaly

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