Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

Publicações por Guilherme Moreira Aresta

2015

Optical Flow Based Approach for Automatic Cardiac Cycle Estimation in Ultrasound Images of the Carotid

Autores
Araujo, T; Aresta, G; Rouco, J; Ferreira, C; Azevedo, E; Campilho, A;

Publicação
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2015)

Abstract
This paper proposes a method to detect a reference frame in an ultrasound video of the carotid artery. This reference frame, usually located at the end of the diastole, is used as the location to measure several vascular biomarkers. Our approach is based on the analysis of the movement of the carotid walls in ultrasound images using an optical flow technique. A periodic movement resembling heart beat is observed in the resulting signals. The comparison of these signals with electrocardiograms validates the proposed method for detecting the reference frame.

2017

Classification of breast cancer histology images using Convolutional Neural Networks

Autores
Araujo, T; Aresta, G; Castro, E; Rouco, J; Aguiar, P; Eloy, C; Polonia, A; Campilho, A;

Publicação
PLOS ONE

Abstract
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

2017

Improving Convolutional Neural Network Design via Variable Neighborhood Search

Autores
Araujo, T; Aresta, G; Almada Lobo, B; Mendonca, AM; Campilho, A;

Publicação
IMAGE ANALYSIS AND RECOGNITION, ICIAR 2017

Abstract
An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification performance. For this purpose, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to effectively represent the solution space in 2D. Then, k-Means clustering divides this representation space having in account the relative distance between neighbors. The algorithm is tested in the CIFAR-10 image dataset. The obtained solution improves the CNN validation loss by over 15% and the respective accuracy by 5%. Moreover, the network shows higher predictive power and robustness, validating our method for the optimization of CNN design.

2017

Detection of juxta-pleural lung nodules in computed tomography images

Autores
Aresta, G; Cunha, A; Campilho, A;

Publicação
Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, Florida, United States, 11-16 February 2017

Abstract
A method to detect juxta-pleural nodules with radius smaller than 5mm is presented. The intensity difference between nodules and parenchymal tissue as well as the nodules' natural roundness are exploited. Solid nodules are detected by selecting an appropriate threshold over a sliding window, whereas sub-solid/non-solid nodules are enhanced using multi-scale Laplacian-of-Gaussian filters. The 2D-wise outputs are combined to 3D, producing a final candidate list. False positive reduction is achieved with fixed rules and supervised learning. The achieved sensitivity is 57% with 4 false positives/scan, increasing to 62% if only solid nodules are considered. © 2017 SPIE.

2016

Effects of hypergravity on the angiogenic potential of endothelial cells

Autores
Costa Almeida, R; Carvalho, DTO; Ferreira, MJS; Aresta, G; Gomes, ME; van Loon, JJWA; Van der Heiden, K; Granja, PL;

Publicação
JOURNAL OF THE ROYAL SOCIETY INTERFACE

Abstract
Angiogenesis, the formation of blood vessels from pre-existing ones, is a key event in pathology, including cancer progression, but also in homeostasis and regeneration. As the phenotype of endothelial cells (ECs) is continuously regulated by local biomechanical forces, studying endothelial behaviour in altered gravity might contribute to new insights towards angiogenesis modulation. This study aimed at characterizing EC behaviour after hypergravity exposure (more than 1g), with special focus on cytoskeleton architecture and capillary-like structure formation. Herein, human umbilical vein ECs (HUVECs) were cultured under two-dimensional and three-dimensional conditions at 3g and 10g for 4 and 16 h inside the large diameter centrifuge at the European Space Research and Technology Centre (ESTEC) of the European Space Agency. Although no significant tendency regarding cytoskeleton organization was observed for cells exposed to high g's, a slight loss of the perinuclear localization of beta-tubulin was observed for cells exposed to 3g with less pronounced peripheral bodies of actin when compared with 1g control cells. Additionally, hypergravity exposure decreased the assembly of HUVECs into capillary-like structures, with a 10g level significantly reducing their organization capacity. In conclusion, short-term hypergravity seems to affect EC phenotype and their angiogenic potential in a time and g-level-dependent manner.

2018

Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography

Autores
Aresta, G; Araujo, T; Jacobs, C; van Ginneken, B; Cunha, A; Ramos, I; Campilho, A;

Publicação
IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES

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.

  • 1
  • 4