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Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach

  • A. Virzì
  • P. Gori
  • C. O. Muller
  • E. Mille
  • Q. Peyrot
  • L. Berteloot
  • N. Boddaert
  • S. Sarnacki
  • I. Bloch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

In this paper, we propose a patch-based deep learning approach to segment pelvic vessels in 3D MRI images of pediatric patients. For a given T2 weighted MRI volume, a set of 2D axial patches are extracted using a limited number of user-selected landmarks. In order to take into account the volumetric information, successive 2D axial patches are combined together, producing a set of pseudo RGB color images. These RGB images are then used as input for a convolutional neural network (CNN), pre-trained on the ImageNet dataset, which results into both segmentation and vessel labeling as veins or arteries. The proposed method is evaluated on 35 MRI volumes of pediatric patients, obtaining an average segmentation accuracy in terms of Average Symmetric Surface Distance of \(ASSD = 0.89 \pm 0.07\) mm and Dice Index of \(DC = 0.79 \pm 0.02\).

Notes

Acknowledgements

A. Virzì, P. Gori, C.O. Muller, E. Mille, Q. Peyrot, L. Berteloot, N. Boddaert, S. Sarnacki and I. Bloch have no conflicts of interest or financial ties to disclose.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • A. Virzì
    • 1
    • 4
  • P. Gori
    • 1
  • C. O. Muller
    • 2
    • 4
  • E. Mille
    • 2
    • 4
  • Q. Peyrot
    • 2
    • 4
  • L. Berteloot
    • 3
    • 4
  • N. Boddaert
    • 3
    • 4
  • S. Sarnacki
    • 2
    • 4
  • I. Bloch
    • 1
    • 4
  1. 1.LTCI, Télécom ParisTech, Université Paris-SaclayParisFrance
  2. 2.Department of Pediatric Surgery, Paris Descartes University, Hôpital Necker Enfants-Malades, Assistance Publique - Hôpitaux de ParisParisFrance
  3. 3.Department of Pediatric Radiology, Paris Descartes University, Hôpital Necker Enfants-Malades, Assistance Publique - Hôpitaux de ParisParisFrance
  4. 4.IMAG2 Laboratory, Imagine InstituteParisFrance

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