Abstract
A new approach for robust edge detection on B-mode ultrasound images of the carotid artery is proposed in this paper. The proposed method uses anisotropic Gaussian derivative filters along with non-maximum suppression over the overall artery wall orientation in local regionS. The anisotropic filters allow using a wider integration scale along the edges while preserving the edge location precision. They also perform edge continuation, resulting in the connection of isolated edge points along linear segments, which is a valuable feature for the segmentation of the artery wall layerS. However, this usually results in false edges being detected near convex contours and isolated pointS. The use of non-maximum suppression over pooled local orientations is proposed to solve this issue. Experimental results are provided to demonstrate that the proposed edge detector outperforms other common methods in the detection of the lumen-intima and media-adventia layer interfaces of the carotid vessel wallS. Additionally, the resulting edges are more continuous and precisely located.

Abstract
We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.

Abstract
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.

Abstract
The segmentation and characterization of the lung lobes are important tasks for Computer Aided Diagnosis (CAD) systems related to pulmonary disease. The detection of the fissures that divide the lung lobes is non-trivial when using classical methods that rely on anatomical information like the localization of the airways and vessels. This work presents a fully automatic and supervised approach to the problem of the segmentation of the five pulmonary lobes from a chest Computer Tomography (CT) scan using a Fully RegularizedV-Net (FRV- Net), a 3D Fully Convolutional Neural Network trained end-to- end. Our network was trained and tested in a custom dataset that we make publicly available. It can correctly separate the lobes even in cases when the fissure is not well delineated, achieving 0.93 in per-lobe Dice Coefficient and 0.85 in the inter-lobar Dice Coefficient in the test set. Both quantitative and qualitative results show that the proposed method can learn to produce correct lobe segmentations even when trained on a reduced dataset.

Abstract
This Special Issue on Electrical and Computer Engineering includes selected papers from the 1st edition of the Symposium on Electrical and Computer Engineering (ECE 2015), one of the symposia included in the 1st Doctoral Congress in Engineering, held at FEUP, 11-12 June, 2015. ECE 2015 was an important forum for presenting the research activities of ECE students, particularly from the Doctoral Program in Electrical and Computer Engineering, at FEUP (PDEEC). ECE 2015 received a total of 42 two-page abstracts. The review process was carried out by members of the Symposium Scientific Committee and other reviewers. Each abstract was reviewed by at least two reviewers, and checked by the Program Committee. 37 abstracts were finally accepted and appear in the Symposium book of abstracts. From the 37 abstracts, 17 were presented in four oral sessions, and 20 in one poster session. We were very pleased to include two keynote talks: “The Internet of Things - Latest Trends and Future Perspectives” by Carlos Azeredo Leme, University of Lisbon, Portugal; “A Perspective on Virtual Radio Access Networks” by Luís M. Correia, University of Lisbon, Portugal. Six papers were invited to submit extended versions to this special issue, that were further reviewed and published in this issue.We would like to sincerely thank the authors for submitting these extended versions, and we thank the special issue reviewers for the careful evaluation and feedback provided to the authors. We also would like to express our gratitude to Luís Miguel Costa, for supporting the organization of this Special issue of the U.Porto Journal of Engineering.Finally, we are very pleased to give the readership of this Special Issue on ECE examples of the research developed by PDEEC students, from a vast area covered by the Electrical and Computer Engineering at FEUP and at the associated research institutes and research centers.

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative - LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value >= 92.16% with a significance level of 5%.