108 research outputs found
Segmentation of lung fields in digital chest radiographs by artificial neural networks
Lung field segmentation is a basic step for virtually any quantitative procedure. In this view, due to the imaging process and the complexity of the imaged district, an efficient use of prior anatomical knowledge is crucial. In this report we describe a new approach to lung field segmentation which is based on fuzzy boundary modeling and a neural network architecture including supervised multilayer networks and topology preserving maps
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
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest
X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is
time-consuming and can be affected by human subjectivity, making it desirable
to design computer-aided systems that assist clinicians in the diagnosis
process. Automatic CTR estimation through chest organ segmentation, however,
requires large amounts of pixel-level annotated data, which is often
unavailable. To alleviate this problem, we propose an unsupervised domain
adaptation framework based on adversarial networks. The framework learns domain
invariant feature representations from openly available data sources to produce
accurate chest organ segmentation for unlabeled datasets. Specifically, we
propose a model that enforces our intuition that prediction masks should be
domain independent. Hence, we introduce a discriminator that distinguishes
segmentation predictions from ground truth masks. We evaluate our system's
prediction based on the assessment of radiologists and demonstrate the clinical
practicability for the diagnosis of cardiomegaly. We finally illustrate on the
JSRT dataset that the semi-supervised performance of our model is also very
promising.Comment: Accepted by MICCAI 201
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