Mask-R 2 CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

Background and objectives: Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R2CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods: Mask-R2CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results: Mask-R2CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R2CNN achieved a mean absolute difference of 1.95 mm (standard deviation = ± 1.92 mm), outperforming other approaches in the literature. Conclusions: With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R2CNN may be an effective support for clinicians for assessing fetal growth

Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7

Preterm Infants' Pose Estimation with Spatio-Temporal Features

Objective: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm infants' limb pose estimation that features spatio-temporal information to detect and track limb joints from depth videos with high reliability. Methods: Limb-pose estimation is performed using a deep-learning framework consisting of a detection and a regression convolutional neural network (CNN) for rough and precise joint localization, respectively. The CNNs are implemented to encode connectivity in the temporal direction through 3D convolution. Assessment of the proposed framework is performed through a comprehensive study with sixteen depth videos acquired in the actual clinical practice from sixteen preterm infants (the babyPose dataset). Results: When applied to pose estimation, the median root mean square distance, computed among all limbs, between the estimated and the ground-truth pose was 9.06 pixels, overcoming approaches based on spatial features only (11.27 pixels). Conclusion: Results showed that the spatio-temporal features had a significant influence on the pose-estimation performance, especially in challenging cases (e.g., homogeneous image intensity). Significance: This article significantly enhances the state of art in automatic assessment of preterm infants' health status by introducing the use of spatio-temporal features for limb detection and tracking, and by being the first study to use depth videos acquired in the actual clinical practice for limb-pose estimation. The babyPose dataset has been released as the first annotated dataset for infants' pose estimation

GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS

Abstract. Point clouds obtained via Terrestrial Laser Scanning (TLS) surveys of historical buildings are generally transformed into semantically structured 3D models with manual and time-consuming workflows. The importance of automatizing this process is widely recognized within the research community. Recently, deep neural architectures have been applied for semantic segmentation of point clouds, but few studies have evaluated them in the Cultural Heritage domain, where complex shapes and mouldings make this task challenging. In this paper, we describe our experiments with the DGCNN architecture to semantically segment historical buildings point clouds, acquired with TLS. We propose a variation of the original approach where a radius distance based technique is used instead of K-Nearest Neighbors (KNN) to represent the neighborhood of points. We show that our approach provides better results by evaluating it on two real TLS point clouds, representing two Italian historical buildings: the Ducal Palace in Urbino and the Palazzo Ferretti in Ancona

Energy harvesting applied to smart shoes

The appeal of energy harvesting systems lies in the possibility of capturing free energy that would be dissipated and is therefore obtainable without costs. Today, advanced techniques and devices exist for capturing from the environment, storing, and managing quotas of natural energy, which are made available in the form of electrical energy. At the same time, the most recent microprocessors grant an extremely high power efficiency, which permits their operation with minimal power consumption. As a consequence, low-consuming devices can be power supplied by using energy harvesting systems. If this concept is applied to wearable electronics, the most efficient choice is that of exploiting the energy released when the users walk, by developing systems that are embedded in the shoe sole. At each step, the force exerted on the device can be transformed into a relatively high amount of electrical energy, for example by using piezoelectric elements and electromagnetic induction systems. The paper describes the design of four different solutions for smart shoes that make use of energy harvesting apparatuses for the power supply of sensors and complex monitoring systems, for example aimed at GPS localization. An initial comparative assessment of the four architectures is reported, by weighing production costs, ease of manufacture and energy harvesting performance

Preterm infants' limb-pose estimation from depth images using convolutional neural networks

Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the literature either provides approaches to whole-body segmentation and tracking, which, however, has poor clinical value, or retrieve a posteriori limb pose from limb segmentation, increasing computational costs and introducing inaccuracy sources. In this paper, we address the problem of limb-pose estimation under a different point of view. We proposed a 2D fully-convolutional neural network for roughly detecting limb joints and joint connections, followed by a regression convolutional neural network for accurate joint and joint-connection position estimation. Joints from the same limb are then connected with a maximum bipartite matching approach. Our analysis does not require any prior modeling of infants' body structure, neither any manual interventions. For developing and testing the proposed approach, we built a dataset of four videos (video length = 90 s) recorded with a depth sensor in a neonatal intensive care unit (NICU) during the actual clinical practice, achieving median root mean square distance [pixels] of 10.790 (right arm), 10.542 (left arm), 8.294 (right leg), 11.270 (left leg) with respect to the ground-truth limb pose. The idea of estimating limb pose directly from depth images may represent a future paradigm for addressing the problem of preterm-infants' movement monitoring and offer all possible support to clinicians in NICUs

Biodrama and Historical Theatre: between Fragment and Translation

Traditional dramatic theatre, based on the Aristotelian idea of mimesis, narrates stories that, regardless of their particular plot, always have a beginning, a middle and an end. This theatre is presented as a mirror of reality. The Aristotelian units order the chaos of human existence and introduce the reassuring illusion of control over time and over life throughout its permanent unfolding. But this Aristotelian notions are called into question in postdramatic theatre, where theatre no longer seeks to be the mirror of reality, its faithful and detailed reproduction. In La escena posdramática [The postdramatic scene] (2017), Beatriz Trastoy explains that contemporary Argentine theatre focuses on metadramatic questions and problematizes theatrical theory and practice. According to the author, postmodern theatre does not reflect upon the characters and their actions, but upon the theatrical activity itself. In this essay, basing ourselves on the notions of “translation” (Trastoy) and “fragment of life” (Jean-Pierre Sarrazac), we analyze two contemporary Argentine plays that spotlight the tensions between fiction and reality: Mi mamá y mi tía by Vivi Tellas and El pasado es un animal grotesco by Mariano Pensotti. El teatro dramático, tradicional, fundado en la noción aristotélica de mimesis, narra historias que, más allá de las distintas tramas que puedan plantear, tendrán siempre comienzo, medio y final. Es un teatro que se propone como doble de la realidad. Las unidades aristotélicas ordenan el caos de la existencia humana e introducen la ilusión tranquilizadora del control sobre el tiempo y sobre la vida en su permanente transcurrir. Sin embargo, la modalidad escénica del teatro posdramático pone en crisis las nociones aristotélicas; este teatro ya no busca ser espejo de la realidad, reproducción fiel y detallada. Beatriz Trastoy, en La escena posdramática (2017), caracteriza al teatro argentino contemporáneo como un teatro que se focaliza en los cuestionamientos metaescénicos, en la problematización de la teoría y la práctica teatrales. En este teatro posmoderno, según la autora, no se reflexiona sobre los personajes y sus acciones: el quehacer teatral es tematizado. Nos interesa trabajar aquí con dos obras del teatro argentino actual que ponen sobre la mesa las tensiones entre ficción y realidad desde las nociones de “traducción” (Trastoy) y “fragmento de vida” (Sarrazac): Mi mamá y mi tía, de Vivi Tellas, y El pasado es un animal grotesco, de Mariano Pensotti