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.
In this paper, we aim at proving the effectiveness of dictionary learning techniques on the task of retinal blood vessel segmentation. We present three different methods based on dictionary learning and sparse coding that reach state-of-the-art results. Our methods are tested on two, well-known, publicly available datasets: DRIVE and STARE. The methods are compared to many state-of-the-art approaches and turn out to be very promising.
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