Abstract
Semantic segmentation consists in predicting whether any given pixel is part of the object of interest or not. Two types of errors are therefore possible: false positives and false negatives. For visualization and emphasis purposes, we might want to put special effort into reducing one type of error in detriment of the other. A common practice is to define the two types of errors as a relative trade-off using a cost matrix. However, it might be more natural for humans to define the trade-off in terms of an absolute constraint on one type of errors while trying to minimize the other. Previously, we suggested possible approaches to introduce this absolute trade-off in binary classifiers. Extending to semantic segmentation, we propose a threshold on the sigmoid layer and modifications to gradient descent such as adding a new term to the loss function and training in two phases. The latter produced the more resilient results, with a simple threshold being sufficient in most cases.

Abstract
The segmentation of blood vessels in medical images has been heavily studied, given its impact in several clinical practices. Deep Learning methods have been applied to supervised segmentation of blood vessels, mainly the retinal ones due to the availability of manual annotations. Despite their success, they typically minimize the Binary Cross Entropy loss, which does not penalize topological mistakes. These errors are relevant in graph-like structures such as blood vessel trees, as a missing segment or an inadequate merging or splitting of branches, may severely change the topology of the network and put at risk the extraction of vessel pathways and their characterization. In this paper, we propose an end-to-end network design comprising a cascade of a typical segmentation network and a Variational Auto-Encoder which, by learning a rich but compact latent space, is able to correct many topological incoherences. Our experiments in three of the most commonly used retinal databases, DRIVE, STARE, and CHASEDB1, show that the proposed model effectively learns representations inducing better segmentations in terms of topology, without hurting the usual pixel-wise metrics.

Abstract
Convolutional Neural Networks (CNN) have become the gold standard in many visual recognition tasks including medical applications. Due to their high variance, however, these models are prone to over-fit the data they are trained on. To mitigate this problem, one of the most common strategies, is to perform data augmentation. Rotation, scaling and translation are common operations. In this work we propose an alternative method to rotation-based data augmentation where the rotation transformation is performed inside the CNN architecture. In each training batch the weights of all convolutional layers are rotated by the same random angle. We validate our proposed method empirically showing its usefulness under different scenarios.

Abstract
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.

Abstract
Rat’s gait analysis plays an important role in the assessment of the impact of certain drugs on the treatment of osteoarthritis. Since movement-evoked pain is an early characteristic of this degenerative joint disease, the affected animal modifies its behavior to protect the injured joint from load while walking, altering its gait’s parameters, which can be detected through a video analysis. Because commercially available video-based gait systems still present many limitations, researchers often choose to develop a customized system for the acquisition of the videos and analyze them manually, a laborious and time-consuming task prone to high user variability. Therefore, and bearing in mind the recent advances in machine learning and computer vision fields, as well as their presence in many tracking and recognition applications, this work is driven by the need to find a solution to automate the detection and quantification of the animal’s gait changes making it an easier, faster, simpler and more robust task. Thus, a comparison between different methodologies to detect and segment the animal under degraded luminance conditions is presented in this paper as well as an algorithm to detect, segment and classify the animal’s paws. © 2019, Springer Nature Switzerland AG.

Abstract
Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met. Deep learning has replaced traditional approaches, achieving state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, with the addition of a quality prediction output. The output from a state-of-the-art base segmenter is refined, simultaneously improving it and trying to predict its quality. We show that the quality concept can be used as a regularizer while training a network for direct segmentation refinement.