71 research outputs found
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
A computer aided diagnosis system for lung nodules detection in postero anterior chest radiographs
This thesis describes a Computer Aided System aimed at lung nodules detection.
The fully automatized method developed to search for nodules is
composed by four steps. They are the segmentation of the lung field, the
enhancement of the image, the extraction of the candidate regions, and the
selection between them of the regions with the highest chance to be True
Positives. The steps of segmentation, enhancement and candidates extraction
are based on multi-scale analysis. The common assumption underlying
their development is that the signal representing the details to be detected
by each of them (lung borders or nodule regions) is composed by a mixture
of more simple signals belonging to different scales and level of details.
The last step of candidate region classification is the most complicate; its
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task is to discern among a high number of candidate regions, the few True
Positives. To this aim several features and different classifiers have been
investigated.
In Chapter 1 the segmentation algorithm is described; the algorithm has
been tested on the images of two different databases, the JSRT and the
Niguarda database, both described in the next section, for a total of 409
images. We compared the results obtained with another method presented
in the literature and described by Ginneken, in [85], as the one obtaining
the best performance at the state of the art; it has been tested on the same
images of the JSRT database. No errors have been detected in the results
obtained by our method, meanwhile the one previously mentioned produced
an overall number of error equal to 50. Also the results obtained on the
images of the Niguarda database confirmed the efficacy of the system realized,
allowing us to say that this is the best method presented so far in
the literature. This sentence is based also on the fact that this is the only
system tested on such an amount of images, and they are belonging to two
different databases.
Chapter 2 is aimed at the description of the multi-scale enhancement and
the extraction methods.
The enhancement allows to produce an image where the \u201cconspicuity\u201d of
nodules is increased, so that nodules of different sizes and located in parts
of the lungs characterized by completely different anatomic noise are more
visible. Based on the same assumption the candidates extraction procedure,
described in the same chapter, employs a multi-scale method to detect all
the nodules of different sizes. Also this step has been compared with two
methods ([8] and [1]) described in the literature and tested on the same
images. Our implementation of the first one of them ([8]) produced really
poor results; the second one obtained a sensitivity ratio (See Appendix C
for its definition) equal to 86%. The considerably better performance of our
method is proved by the fact that the sensitivity ratio we obtained is much
higher (it is equal to 97%) and also the number of False positives detected
is much less.
The experiments aimed at the classification of the candidates are described
in chapter 3; both a rule based technique and 2 learning systems, the Multi
Layer Perceptron (MLP) and the Support Vector Machine (SVM), have
been investigated. Their input is a set of 16 features. The rule based system
obtained the best performance: the cardinality of the set of candidates left is
highly reduced without lowering the sensitivity of the system, since no True
Positive region is lost. It can be added that this performance is much better
than the one of the system used by Ginneken and Schilam in [1], since its
sensitivity is lower (equal to 77%) and the number of False Positive left is
comparable. The drawback of a rule based system is the need of setting the
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thresholds used by the rules; since they are experimentally set the system is
dependent on the images used to develop it. Therefore it may happen that,
on different databases, the performance could not be so good.
The result of the MLPs and of the SVMs are described in detail and the
ROC analysis is also reported, regarding the experiments performed with
the SVMs.
Furthermore, the attempt to improve the performance of the classification
leaded to other experiments employing SVMs trained with more complicate
feature sets. The results obtained, since not better than the previous,
showed the need of a proper selection of the features. Future works will then
be focused at testing other sets of features, and their combination obtained
by means of proper feature selection techniques
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Lung cancer is the leading cause of cancer death and early diagnosis is
associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive
imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to
distinguish from vascular and bone structures using CXR. Computer vision has
previously been proposed to assist human radiologists in this task, however,
leading studies use down-sampled images and computationally expensive methods
with unproven generalization. Instead, this study localizes lung nodules using
efficient encoder-decoder neural networks that process full resolution images
to avoid any signal loss resulting from down-sampling. Encoder-decoder networks
are trained and tested using the JSRT lung nodule dataset. The networks are
used to localize lung nodules from an independent external CXR dataset.
Sensitivity and false positive rates are measured using an automated framework
to eliminate any observer subjectivity. These experiments allow for the
determination of the optimal network depth, image resolution and pre-processing
pipeline for generalized lung nodule localization. We find that nodule
localization is influenced by subtlety, with more subtle nodules being detected
in earlier training epochs. Therefore, we propose a novel self-ensemble model
from three consecutive epochs centered on the validation optimum. This ensemble
achieved a sensitivity of 85% in 10-fold internal testing with false positives
of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6
following morphological false positive reduction. This result is comparable to
more computationally complex systems based on linear and spatial filtering, but
with a sub-second inference time that is faster than other methods. The
proposed algorithm achieved excellent generalization results against an
external dataset with sensitivity of 77% at a false positive rate of 7.6
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
Computer-aided detection of lung nodules: A review
We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas
A computerized scheme for lung nodule detection in multiprojection chest radiography: Lung nodule detection in multiprojection chest radiography
Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists’ performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme
Identificação de regiões nodulares em imagens digitais de radiografia pulmonar
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200
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