Abstract
Due to inevitable differences between the data used for training modern CAD systems and the data encountered when they are deployed in clinical scenarios, the ability to automatically assess the quality of predictions when no expert annotation is available can be critical. In this paper, we propose a new method for quality assessment of retinal vessel tree segmentations in the absence of a reference ground-truth. For this, we artificially degrade expert-annotated vessel map segmentations and then train a CNN to predict the similarity between the degraded images and their corresponding ground-truths. This similarity can be interpreted as a proxy to the quality of a segmentation. The proposed model can produce a visually meaningful quality score, effectively predicting the quality of a vessel tree segmentation in the absence of a manually segmented reference. We further demonstrate the usefulness of our approach by applying it to automatically find a threshold for soft probabilistic segmentations on a per-image basis. For an independent state-of-the-art unsupervised vessel segmentation technique, the thresholds selected by our approach lead to statistically significant improvements in F1-score (+2.67%) and Matthews Correlation Coefficient (+3.11%) over the thresholds derived from ROC analysis on the training set. The score is also shown to correlate strongly with F1 and MCC when a reference is available.

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. © 2018 The Author(s).

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. © 2018 IEEE.

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.