Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques

Sekou, Taibou Birgui; Hidane, Moncef; Olivier, Julien; Cardot, Hubert

doi:10.1007/978-3-319-67561-9_9

Cited by 2 publications

(1 citation statement)

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“…The same idea of structured prediction is employed in [4] to train a FCN using patches extracted from the training images. In [6,7], patch-based methods are proposed using discriminative dictionary learning techniques for RBVS.…”

Section: Introductionmentioning

confidence: 99%

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

Sekou

Hidane

Olivier

et al. 2018

Machine Learning in Medical Imaging

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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 D, a first network NP is designed and trained using patches extracted from D. Then, NP is used to pre-train a FCN NI to be trained on the full sized images of D. Experimental results are presented on the task of retinal blood vessel segmentation using the well known publicly available DRIVE database.

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Section: Introductionmentioning

confidence: 99%