Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

The purpose of this study was to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system, on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images, by using two different MDCT scanners. The computerized scheme was based on the iris filter. We have collected CT cases of patients with pulmonary nodules. We have included in the study one hundred and thirty-two calcified and noncalcified nodules, measuring 4-30 mm in diameter. CT examinations were performed by using two different equipments: a CT scanner (SOMATOM Emotion 6), and a dual-source computed tomography system (SOMATOM Definition) (Siemens Medical System, Forchheim, Germany), with the following parameters: collimation, 6x1.0mm (Emotion 6); and 64×0.6mm (Definition); 100-130 kV; 70-110 mAs. Data were reconstructed with a slice thickness of 1.25mm (Emotion 6) and 1mm (Definition). True positive cases were determined by an independent interpretation of the study by three experienced chest radiologists, the panel decision being used as the reference standard. Free-response Receiver Operating Characteristic curves, sensitivity and number of false-positive per scan, were calculated. Our CAD scheme, for the test set of the study, yielded a sensitivity of 80%, with an average of 5.2 FPs per examination. At an average false positive rate of 9 per scan, our CAD scheme achieved sensitivities of 94% for all nodules, 94.5% for solid, 80% for non-solid, 84% for spiculated, and 97% for non-spiculated nodules. These encouraging results suggest that our CAD system, advocated as a second reader, may help radiologists in the detection of lung nodules in MDCTThis work has been partially supported by the Xunta de Galicia (expte. nº PGIDIT06BTF20802PR), and by the FIS (expte. nº PI060058) and (expte. nº PI080072)S

Computerâ aided detection of breast masses on full field digital mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134868/1/mp7327.pd

Robust iris recognition under unconstrained settings

Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Robust Vision-Based Pose Correction for a Robotic Manipulator Using Active Markers

Robots with elastic or lightweight components are becoming common in research, but can suffer from undesired positioning imprecision, which motivates a vision-based pose correction of the manipulator. For robotic manipulators that operate outdoors and under changing illumination conditions, robustness of the vision components is of principal concern. We propose a monocular manipulator pose correction based on active markers which are detected by convergence criteria on the image gradient field. We show the capabilities of the method in several outdoor and indoor experiments, considering the use case of a planetary exploration rover prototype equipped with a lightweight robotic arm. The vision-based manipulator pose correction method proves to be successful despite back light, reflections, and image overexposure and additionally allows continued robot operation in the case of extrinsic camera decalibration

Detecção de núcleos de células em sequências de imagens de microscopia confocal

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Classificação de nódulos pulmonares baseada em redes neurais convolucionais profundas em radiografias

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O câncer de pulmão, que se caracteriza pela presença de nódulos, é o tipo mais comum de câncer em todo o mundo, além de ser um dos mais agressivos e fatais, com 20% da mortalidade total por câncer. A triagem do câncer de pulmão pode ser realizada por radiologistas que analisam imagens de raios-X de tórax (CXR). No entanto, a detecção de nódulos pulmonares é uma tarefa difícil devido a sua grande variabilidade, limitações humanas de memória, distração e fadiga, entre outros fatores. Essas dificuldades motivam o desenvolvimento de sistemas de diagnóstico por computador (CAD) para apoiar radiologistas na detecção de nódulos pulmonares. A classificação do nódulo do pulmão é um dos principais tópicos relacionados aos sistemas de CAD. Embora as redes neurais convolucionais (CNN) tenham demonstrado um bom desempenho em muitas tarefas, há poucas explorações de seu uso para classificar nódulos pulmonares em imagens CXR. Neste trabalho, propusemos e analisamos um arcabouço para a detecção de nódulos pulmonares em imagens de CXR que inclui segmentação da área pulmonar, localização de nódulos e classificação de nódulos candidatos. Apresentamos um método para classificação de nódulos candidatos com CNNs treinadas a partir do zero. A eficácia do nosso método baseia-se na seleção de parâmetros de aumento de dados, no projeto de uma arquitetura CNN especializada, no uso da regularização de dropout na rede, inclusive em camadas convolucionais, e no tratamento da falta de amostras de nódulos em comparação com amostras de fundo, balanceando mini-lotes em cada iteração da descida do gradiente estocástico. Todas as decisões de seleção do modelo foram tomadas usando-se um subconjunto de imagens CXR da base Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) separadamente. Então, utilizamos todas as imagens com nódulos no conjunto de dados da Japanese Society of Radiological Technology (JSRT) para avaliação. Nossos experimentos mostraram que as CNNs foram capazes de alcançar resultados competitivos quando comparados com métodos da literatura. Nossa proposta obteve uma curva de operação (AUC) de 7.51 considerando 10 falsos positivos por imagem (FPPI) e uma sensibilidade de 71.4% e 81.0% com 2 e 5 FPPI, respectivamenteAbstract: Lung cancer, which is characterized by the presence of nodules, is the most common type of cancer around the world, as well as one of the most aggressive and deadliest cancer, with 20% of total cancer mortality. Lung cancer screening can be performed by radiologists analyzing chest X-ray (CXR) images. However, the detection of lung nodules is a difficult task due to their wide variability, human limitations of memory, distraction and fatigue, among other factors. These difficulties motivate the development of computer-aided diagnosis (CAD) systems for supporting radiologists in detecting lung nodules. Lung nodule classification is one of the main topics related to CAD systems. Although convolutional neural networks (CNN) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in CXR images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology (JSRT) dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic (AUC) curve of 7.51 considering 10 false positives per image (FPPI), and a sensitivity of 71.4% and 81.0% with 2 and 5 FPPI, respectivelyMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE