264 research outputs found
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
Lung segmentation in chest X-rays (CXRs) is an important prerequisite for
improving the specificity of diagnoses of cardiopulmonary diseases in a
clinical decision support system. Current deep learning (DL) models for lung
segmentation are trained and evaluated on CXR datasets in which the
radiographic projections are captured predominantly from the adult population.
However, the shape of the lungs is reported to be significantly different for
pediatrics across the developmental stages from infancy to adulthood. This
might result in age-related data domain shifts that would adversely impact lung
segmentation performance when the models trained on the adult population are
deployed for pediatric lung segmentation. In this work, our goal is to analyze
the generalizability of deep adult lung segmentation models to the pediatric
population and improve performance through a systematic combinatorial approach
consisting of CXR modality-specific weight initializations, stacked
generalization, and an ensemble of the stacked generalization models. Novel
evaluation metrics consisting of Mean Lung Contour Distance and Average Hash
Score are proposed in addition to the Multi-scale Structural Similarity Index
Measure, Intersection of Union, and Dice metrics to evaluate segmentation
performance. We observed a significant improvement (p < 0.05) in cross-domain
generalization through our combinatorial approach. This study could serve as a
paradigm to analyze the cross-domain generalizability of deep segmentation
models for other medical imaging modalities and applications.Comment: 11 pages, 7 figures, and 8 table
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
The development of successful artificial intelligence models for chest X-ray
analysis relies on large, diverse datasets with high-quality annotations. While
several databases of chest X-ray images have been released, most include
disease diagnosis labels but lack detailed pixel-level anatomical segmentation
labels. To address this gap, we introduce an extensive chest X-ray multi-center
segmentation dataset with uniform and fine-grain anatomical annotations for
images coming from six well-known publicly available databases: CANDID-PTX,
ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in
676,803 segmentation masks. Our methodology utilizes the HybridGNet model to
ensure consistent and high-quality segmentations across all datasets. Rigorous
validation, including expert physician evaluation and automatic quality
control, was conducted to validate the resulting masks. Additionally, we
provide individualized quality indices per mask and an overall quality
estimation per dataset. This dataset serves as a valuable resource for the
broader scientific community, streamlining the development and assessment of
innovative methodologies in chest X-ray analysis. The CheXmask dataset is
publicly available at:
\url{https://physionet.org/content/chexmask-cxr-segmentation-data/}.Comment: The CheXmask dataset is publicly available at
https://physionet.org/content/chexmask-cxr-segmentation-data
TB detection using modified Local Binary Pattern features
Abstract: This paper explores a computer-aided detection scheme to aid radiologists in making a higher percentage of correct diagnoses when analysing chest radiographs. The approach undertaken in the detection process is to use several proprietary image processing algorithms to adjust, segment and classify a radiograph. Firstly, a Difference of Gaussian (DoG) energy normalisation method is applied to the image. By doing this, the effect of differing equipment and calibrations is normalised. Thereafter, the lung area is detected using Active Shape Models (ASMs). Once identified, the lungs are analysed using Local Binary Patterns (LBPs). This technique is combined with a probability measure that makes use of the the locations of known abnormalities in the training dataset. The results of the segmentation when compared to ground truth masks achieves an overlap segmentation accuracy of 87,598±3,986%. The challenges faced during classification are also discussed
Evaluation of the diagnostic accuracy of computer-aided detection of tuberculosis on chest radiography among private sector patients in Pakistan
The introduction of digital CXR with automated computer-aided interpretation, has given impetus to the role of CXR in TB screening, particularly in low resource, high-burden settings. The aim of this study was to evaluate the diagnostic accuracy of CAD4TB as a screening tool, implemented in the private sector in Karachi, Pakistan. This study analyzed retrospective data from CAD4TB and Xpert MTB/RIF testing carried out at two private TB treatment and diagnostic centers in Karachi. Sensitivity, specificity, potential Xperts saved, were computed and the receiver operator characteristic curves were constructed for four different models of CAD4TB. A total of 6,845 individuals with presumptive TB were enrolled in the study, 15.2% of which had MTB + ve result on Xpert. A high sensitivity (range 65.8-97.3%) and NPV (range 93.1-98.4%) were recorded for CAD4TB. The Area under the ROC curve (AUC) for CAD4TB was 0.79. CAD4TB with patient demographics (age and gender) gave an AUC of 0.83. CAD4TB offered high diagnostic accuracy. In low resource settings, CAD4TB, as a triage tool could minimize use of Xpert. Using CAD4TB in combination with age and gender data enhanced the performance of the software. Variations in demographic information generate different individual risk probabilities for the same CAD4TB scores
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