Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening.

Abstract
Speckle tracking echocardiography (STE) is a clinical tool to noninvasively assess regional myocardial function through the quantification of regional motion and deformation. Even if the time resolution of STE can be improved by high-frame-rate (HFR) imaging, dedicated HFR STE algorithms have to be developed to detect very small interframe motions. Therefore, in this article, we propose a novel 2-D STE method, purposely developed for HFR echocardiography. The 2-D motion estimator consists of a two-step algorithm based on the 1-D cross correlations to separately estimate the axial and lateral displacements. The method was first optimized and validated on simulated data giving an accuracy of 3.3% and 10.5% for the axial and lateral estimates, respectively. Then, it was preliminarily tested in vivo on ten healthy volunteers showing its clinical applicability and feasibility. Moreover, the extracted clinical markers were in the same range as those reported in the literature. Also, the estimated peak global longitudinal strain was compared with that measured with a clinical scanner showing good correlation and negligible differences (-20.94% versus -20.31%, ${p}$ -value = 0.44). In conclusion, a novel algorithm for STE was developed: the radio frequency (RF) signals were preferred for the axial motion estimation, while envelope data were preferred for the lateral motion. Furthermore, using 2-D kernels, even for 1-D cross correlation, makes the method less sensitive to noise. © 1986-2012 IEEE.

Abstract
Background and Objective: Glaucoma, an eye condition that leads to permanent blindness, is typically asymptomatic and therefore difficult to be diagnosed in time. However, if diagnosed in time, Glaucoma can effectively be slowed down by using adequate treatment; hence, an early diagnosis is of utmost importance. Nonetheless, the conventional approaches to diagnose Glaucoma adopt expensive and bulky equipment that requires qualified experts, making it difficult, costly and time-consuming to diagnose large amounts of people. Consequently, new alternatives to diagnose Glaucoma that suppress these issues should be explored. Methods: This work proposes an interpretable computer-aided diagnosis (CAD) pipeline that is capable of diagnosing Glaucoma using fundus images and run offline in mobile devices. Several public datasets of fundus images were merged and used to build Convolutional Neural Networks (CNNs) that perform segmentation and classification tasks. These networks are then used to build a pipeline for Glaucoma assessment that outputs a Glaucoma confidence level and also provides several morphological features and segmentations of relevant structures, resulting in an interpretable Glaucoma diagnosis. To assess the performance of this method in a restricted environment, this pipeline was integrated into a mobile application and time and space complexities were assessed. Results: Considering the test set, the developed pipeline achieved 0.91 and 0.75 of Intersection over Union (IoU) in the optic disc and optic cup segmentation, respectively. With regards to the classification, an accuracy of 0.87 with a sensitivity of 0.85 and an AUC of 0.93 were attained. Moreover, this pipeline runs on an average Android smartphone in under two seconds. Conclusions: The results demonstrate the potential that this method can have in the contribution to an early Glaucoma diagnosis. The proposed approach achieved similar or slightly better metrics than the current CAD systems for Glaucoma assessment while running on more restricted devices. This pipeline can, therefore, be used to construct accurate and affordable CAD systems that could enable large Glaucoma screenings, contributing to an earlier diagnose of this condition. © 2020

Abstract

Abstract
Rehabilitation is a relevant process for the recovery from dysfunctions and improves the realization of patient's Activities of Daily Living (ADLs). Robotic systems are considered an important field within the development of physical rehabilitation, thus allowing the collection of several data, besides performing exercises with intensity and repeatedly. This paper addresses the use of a collaborative robot applied in the rehabilitation field to help the physiotherapy of upper limb of patients, specifically shoulder. To perform the movements with any patient the system must learn to behave to each of them. In this sense, the Reinforcement Learning (RL) algorithm makes the system robust and independent of the path of motion. To test this approach, it is proposed a simulation with a UR3 robot implemented in V-REP platform. The main control variable is the resistance force that the robot is able to do against the movement performed by the human arm.

Abstract