Application to quantify fetal lung branching on rat explants

Recently, regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. The development of accurate and reliable segmentation techniques may be essential to improve research outcomes. This work presents an image processing method to measure the perimeter and area of lung branches on fetal rat explants. The algorithm starts by reducing the noise corrupting the image with a pre-processing stage. The outcome is input to a watershed operation that automatically segments the image into primitive regions. Then, an image pixel is selected within the lung explant epithelial, allowing a region growing between neighbouring watershed regions. This growing process is controlled by a statistical distribution of each region. When compared with manual segmentation, the results show the same tendency for lung development. High similarities were harder to obtain in the last two days of culture, due to the increased number of peripheral airway buds and complexity of lung architecture. However, using semiautomatic measurements, the standard deviation was lower and the results between independent researchers were more coherent

Application to quantify fetal lung branching on rat explant

Recently, regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. The development of accurate and reliable segmentation techniques may be essential to improve research outcomes. This work presents an image processing method to measure the perimeter and area of lung branches on fetal rat explants. The algorithm starts by reducing the noise corrupting the image with a pre-processing stage. The outcome is input to a watershed operation that automatically segments the image into primitive regions. Then, an image pixel is selected within the lung explant epithelial, allowing a region growing between neighbouring watershed regions. This growing process is controlled by a statistical distribution of each region. When compared with manual segmentation, the results show the same tendency for lung development. High similarities were harder to obtain in the last two days of culture, due to the increased number of peripheral airway buds and complexity of lung architecture. However, using semiautomatic measurements, the standard deviation was lower and the results between independent researchers were more coherent.

Mechatronic system for performing blood pre-transfusion tests

The blood types determination is essential to perform safe blood transfusions. In emergency situations isadministrated the “universal donor” blood type. However, sometimes, this blood type can cause incom-patibilities in the transfusion receptor. A mechatronic prototype was developed to solve this problem.The prototype was built to meet specific goals, incorporating all the necessary components. The obtainedsolution is close to the final system that will be produced later, at industrial scale, as a medical device.The prototype is a portable and low cost device, and can be used in remote locations. A computer appli-cation, previously developed is used to operate with the developed mechatronic prototype, and obtainautomatically test results. It allows image acquisition, processing and analysis, based on Computer Visionalgorithms, Machine Learning algorithms and deterministic algorithms. The Machine Learning algorithmsenable the classification of occurrence, or alack of agglutination in the mixture (blood/reagents), and amore reliable and a safer methodology as test data are stored in a database. The work developed allowsthe administration of a compatible blood type in emergency situations, avoiding the discontinuity of the“universal donor” blood type stocks, and reducing the occurrence of human errors in the transfusion practice.

A brief survey on online analysis of movement data

The increasing availability of mobility data and the awareness of its importance and value have been motivating many researchers to the development of models and tools for analyzing movement data. This paper presents a brief survey of significant research works about modeling, processing and visualization of data about moving objects. We identified some key research fields that will provide better features for online analysis of movement data. As result of the literature review, we suggest a generic multi-layer architecture for the development of an online analysis processing software tool, which will be used for the definition of the future work of our team.

Variations of the soft tissue thicknesses external to the ribs in Pectus Excavatum patients

Background: Surgical repair of pectus excavatum (PE) has become more popular due to improvements in the minimally invasive Nuss procedure. The pre-surgical assessment of PE patients requires Computerized Tomography (CT), as the malformation characteristics vary from patient to patient. Objective: This work aims to characterize soft tissue thickness (STT) external to the ribs among PE patients. It also presents a comparative analysis between the anterior chest wall surface before and after surgical correction. Methods: Through surrounding tissue segmentation in CT data, STT values were calculated at different lines along the thoracic wall, with a reference point in the intersection of coronal and median planes. The comparative analysis between the two 3D anterior chest surfaces sets a surgical correction influence area (SCIA) and a volume of interest (VOI) based on image processing algorithms, 3D surface algorithms, and registration methods. Results: There are always variations between left and right side STTs (2.54±2.05 mm and 2.95±2.97 mm for female and male patients, respectively). STTs are dependent on age, sex, and body mass index of each patient. On female patients, breast tissue induces additional errors in bar manual

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Variations of the soft tissue thicknesses external to the ribs in pectus excavatum patients

Background: Surgical repair of pectus excavatum (PE) has become more popular due to improvements in the minimally invasive Nuss procedure. The pre-surgical assessment of PE patients requires Computerized Tomography (CT), as the malformation characteristics vary from patient to patient. Objective: This work aims to characterize soft tissue thickness (STT) external to the ribs among PE patients. It also presents a comparative analysis between the anterior chest wall surface before and after surgical correction. Methods: Through surrounding tissue segmentation in CT data, STT values were calculated at different lines along the thoracic wall, with a reference point in the intersection of coronal and median planes. The comparative analysis between the two 3D anterior chest surfaces sets a surgical correction influence area (SCIA) and a volume of interest (VOI) based on image processing algorithms, 3D surface algorithms, and registration methods. Results: There are always variations between left and right side STTs (2.54±2.05 mm and 2.95±2.97 mm for female and male patients, respectively). STTs are dependent on age, sex, and body mass index of each patient. On female patients, breast tissue induces additional errors in bar manual

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Residents’ perceptions on impacts of hosting the “Guimarães 2012 European Capital of Culture”: comparisons of the pre-and post-2012 ECOC

Residents tend to have high expectations about the benefits of hosting a mega‐event. So, it was not surprising that the nomination of Guimarães, Portugal, as the 2012 European Capital of Culture (2012 ECOC) had raised great expectations in the local community towards its socio‐economic and cultural benefits. The present research was designed to examine the Guimarães residents’ perceptions on the impacts of hosting the 2012 ECOC approached in two different time schedules, the pre‐ and the post‐event, trying to capture the evolution of the residents` evaluation of its impacts. For getting the data, two surveys were applied to Guimarães` residents, one in the pre‐event phase, in 2011, and another in the post‐event phase, in 2013. This approach is uncommonly applied to Portugal data and it is even the first time it was done to a Portuguese European Capital of Culture. After a factor analysis, the results of t‐tests indicate that there were significant differences (p<0.05) between the samples from the pre‐ and post‐2012 ECOC on two positive impact factors (Community’ benefits and Residents’ benefits) and one negative impact factor (Economic, social and environmental costs). Respondents also showed a negative perception of the impacts in all dimensions, except Changes in habits of Guimarães residents.

Improving The Robustness Of Interventional 4D Ultrasound Segmentation Through The Use Of Personalized Shape Priors

While fluoroscopy is still the most widely used imaging modality to guide cardiac interventions, the fusion of pre-operative Magnetic Resonance Imaging (MRI) with real-time intra-operative ultrasound (US) is rapidly gaining clinical acceptance as a viable, radiation-free alternative. In order to improve the detection of the left ventricular (LV) surface in 4D ultrasound, we propose to take advantage of the pre-operative MRI scans to extract a realistic geometrical model representing the patients cardiac anatomy. This could serve as prior information in the interventional setting, allowing to increase the accuracy of the anatomy extraction step in US data. We have made use of a real-time 3D segmentation framework used in the recent past to solve the LV segmentation problem in MR and US data independently and we take advantage of this common link to introduce the prior information as a soft penalty term in the ultrasound segmentation algorithm. We tested the proposed algorithm in a clinical dataset of 38 patients undergoing both MR and US scans. The introduction of the personalized shape prior improves the accuracy and robustness of the LV segmentation, as supported by the error reduction when compared to core lab manual segmentation of the same US sequences.