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.

Structural defect detection based on finite element model (FEM) updating is an optimization problem by minimizing the discrepancy of responses between model and measurement. Researchers have introduced many methods to perform the FEM updating for defect detection of the structures. A popular approach is to adopt the particle swarm optimization (PSO) algorithm. In this process, the fitness function is a critical factor in the success of the PSO-FEM approach. Our objective is to compare the performances of four fitness functions based on natural frequencies using the standard PSO-FEM approach for defect detection. In this paper, the definition of the standard PSO algorithm is first presented. After constructing the finite element benchmark model of the beam structure, four commonly used fitness functions based on natural frequencies are outlined. Their performance in defect detection of beam structures will be evaluated using the standard PSO-FEM approach. Finally, in the numerical simulations, the population diversity, success rate, mean iterations, and CPU time of the four fitness functions for the algorithm are calculated. The simulation results comprehensively evaluate their performances for single defect and multidefect scenario, and the effectiveness and superiority of the fitness function S 4 will be demonstrated.

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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.