333 research outputs found
Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging
Includes abstract.Includes bibliographical references.This project proposed to implement a bony structure suppression tool and analyse its effects on a texture-based classification algorithm in order to assist in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the accuracy of diagnosis. Overlapping structures in chest X-ray images hinder the ability of lung texture analysis for CAD to detect abnormalities. This dissertation examines whether the performance of texturebased CAD tools may be improved by the suppression of bony structures, particularly of the ribs, in the chest region
Proceedings Virtual Imaging Trials in Medicine 2024
This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography
Osteoporosis is a prevalent bone disease that causes fractures in fragile
bones, leading to a decline in daily living activities. Dual-energy X-ray
absorptiometry (DXA) and quantitative computed tomography (QCT) are highly
accurate for diagnosing osteoporosis; however, these modalities require special
equipment and scan protocols. To frequently monitor bone health, low-cost,
low-dose, and ubiquitously available diagnostic methods are highly anticipated.
In this study, we aim to perform bone mineral density (BMD) estimation from a
plain X-ray image for opportunistic screening, which is potentially useful for
early diagnosis. Existing methods have used multi-stage approaches consisting
of extraction of the region of interest and simple regression to estimate BMD,
which require a large amount of training data. Therefore, we propose an
efficient method that learns decomposition into projections of bone-segmented
QCT for BMD estimation under limited datasets. The proposed method achieved
high accuracy in BMD estimation, where Pearson correlation coefficients of
0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD
estimation tasks, respectively, and the root mean square of the coefficient of
variation values were 3.27 to 3.79% for four measurements with different poses.
Furthermore, we conducted extensive validation experiments, including
multi-pose, uncalibrated-CT, and compression experiments toward actual
application in routine clinical practice.Comment: 20 pages and 22 figure
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
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