12 documents found, page 1 of 2
Prediction of stroke lesion at 90-Day follow-up by fusing raw DSC-MRI with para...
Pinto, Adriano; Amorim, Joana; Hakim, Arsany; Alves, Victor; Reyes, Mauricio; Silva, Carlos A.Stroke is the second most common cause of death in developed countries. Rapid clinical assessment and intervention have a major impact on preventing infarct growth and consequently on patients' quality of life. Clinical interventions aim to restore perfusion deficits via pharmaceutical or mechanical intervention. Regardless of which reperfusion procedure is used, clinicians need to consider the risks and benefi...
Combining unsupervised and supervised learning for predicting the final stroke ...
Pinto, Adriano; Pereira, Sérgio; Meier, Raphael; Wiest, Roland; Alves, Victor; Reyes, Mauricio; Silva, Carlos A.Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging ac...
Multi-stage Deep Layer Aggregation for brain tumor segmentation
Silva, Carlos A.; Pinto, Adriano; Pereira, Sérgio; Lopes, AnaGliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inpu...
Adaptive feature recombination and recalibration for semantic segmentation with...
Pereira, Sergio; Pinto, Adriano; Amorim, Joana; Ribeiro, Alexandrine; Alves, Victor; Silva, Carlos A.Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts infor...
Towards using memoization for saving energy in android
Rua, Rui; Couto, Marco Domingos Mendes; Pinto, Adriano; Cunha, Jácome; Saraiva, JoãoOver the last few years, the interest in the analysis of the energy consumption of Android applications has been increasing significantly. Indeed, there are a considerable number of studies which aim at analyzing the energy consumption in the Android ecosystem, such as measuring/estimating the energy consumed by an application or block of code, or even detecting energy expensive coding patterns or APIs. In this...
Segmentation squeeze-and-excitation blocks in stroke lesion outcome prediction
Amorim, Joana; Pinto, Adriano; Pereira, Sergio; Silva, Carlos A.Multi-modal Magnetic Resonance Imaging sequences along with 4D Perfusion Weighted Imaging scans provide important information for stroke lesion outcome prediction. However, the proposed methodologies until now were not able to discriminate correctly the most informative features from the less useful ones. In this work, we propose an enhanced version of a data fusion method for stroke tissue outcome prediction b...
Stroke lesion outcome prediction based on MRI imaging combined with clinical in...
Pinto, Adriano; Mckinley, Richard; Alves, Victor; Wiest, Roland; Silva, Carlos A.; Reyes, MauricioIn developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion ...
Hierarchical brain tumour segmentation using extremely randomized trees
Pinto, Adriano; Pereira, Sérgio; Rasteiro, Deolinda; Silva, Carlos A.Gliomas are the most common and aggressive primary brain tumours, with a short-life expectancy in their highest grade. Magnetic Resonance Imaging is the most common imaging technique to assess brain tumours. However, performing manual segmentation is a difficult and tedious task, mainly due to the large amount of information to be analysed. Therefore, there is a need for automatic and robust segmentation method...
Enhancing clinical MRI perfusion maps with data-driven maps of complementary na...
Pinto, Adriano; Pereira, Sergio; Meier, Raphael; Alves, Victor; Wiest, Roland; Silva, Carlos A.Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient’s life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the ...
Automatic brain tissue segmentation in MR images using Random Forests and Condi...
Pereira, Sergio; Pinto, Adriano; Oliveira, Jorge; Mendrik, Adrienne M.; Correia, J. H.; Silva, Carlos A.Background: The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary.New m...