693 research outputs found
Computer-aided diagnosis in chest radiography
Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health
and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for
texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an
automatic system for detecting regions with abnormal texture, that is applied to a database of images from a
tuberculosis screening program
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification
or segmentation is challenging due to limited images covering different disease
types and severity. We propose an active learning (AL) framework to select most
informative samples and add to the training data. We use conditional generative
adversarial networks (cGANs) to generate realistic chest xray images with
different disease characteristics by conditioning its generation on a real
image sample. Informative samples to add to the training set are identified
using a Bayesian neural network. Experiments show our proposed AL framework is
able to achieve state of the art performance by using about 35% of the full
dataset, thus saving significant time and effort over conventional methods
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Accurate segmentation of anatomical structures in chest radiographs is
essential for many computer-aided diagnosis tasks. In this paper we investigate
the latest fully-convolutional architectures for the task of multi-class
segmentation of the lungs field, heart and clavicles in a chest radiograph. In
addition, we explore the influence of using different loss functions in the
training process of a neural network for semantic segmentation. We evaluate all
models on a common benchmark of 247 X-ray images from the JSRT database and
ground-truth segmentation masks from the SCR dataset. Our best performing
architecture, is a modified U-Net that benefits from pre-trained encoder
weights. This model outperformed the current state-of-the-art methods tested on
the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6%
for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image
Analysis (TIA), MICCAI 201
Cyanotoxin profiling in the subalpine district lakes
Contains fulltext :
193062.pdf (Publisher’s version ) (Open Access
Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children
Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either 'tuberculosis' or 'not tuberculosis'. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of 'tuberculosis' and 40 with 'not tuberculosis') were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify 'tuberculosis' versus 'not tuberculosis' on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis
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