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Retinal Blood Vessel Segmentation Using a Fully Convolutional Network – Transfer Learning from Patch- to Image-Level

  • Taibou Birgui Sekou
  • Moncef Hidane
  • Julien Olivier
  • Hubert Cardot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Fully convolutional networks (FCNs) are well known to provide state-of-the-art results in various medical image segmentation tasks. However, these models usually need a tremendous number of training samples to achieve good performances. Unfortunately, this requirement is often difficult to satisfy in the medical imaging field, due to the scarcity of labeled images. As a consequence, the common tricks for FCNs’ training go from data augmentation and transfer learning to patch-based segmentation. In the latter, the segmentation of an image involves patch extraction, patch segmentation, then patch aggregation. This paper presents a framework that takes advantage of all these tricks by starting with a patch-level segmentation which is then extended to the image level by transfer learning. The proposed framework follows two main steps. Given a image database \(\mathcal {D}\), a first network \(\mathcal {N}_P\) is designed and trained using patches extracted from \(\mathcal {D}\). Then, \(\mathcal {N}_P\) is used to pre-train a FCN \(\mathcal {N}_I\) to be trained on the full sized images of \(\mathcal {D}\). Experimental results are presented on the task of retinal blood vessel segmentation using the well known publicly available DRIVE database.

Keywords

Retinal blood vessel segmentation Fully convolutional neural networks Transfer learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Taibou Birgui Sekou
    • 1
    • 3
  • Moncef Hidane
    • 1
    • 3
  • Julien Olivier
    • 1
    • 3
  • Hubert Cardot
    • 2
    • 3
  1. 1.Institut National des Sciences Appliquées Centre Val de LoireBloisFrance
  2. 2.Université de ToursToursFrance
  3. 3.LIFAT EA 6300ToursFrance

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