546 research outputs found
Identification of space-occupying lesions in medical imaging of the kidney: A review.
Usually, the kidneys can be affected by renal masses or space-occupying lesions (LOE). When reference is made to the term renal mass, all benign and malignant processes that occupy, distort and affect the renal parenchyma and its environment are included, regardless of etiology, shape and volume. Therefore, renal masses include all cystic formations (abscesses), calculi, pseudotumors, neoplasms, inflammatory diseases and traumatic lesions. Thus, for the evaluation of cystic renal masses in medical imaging, according to their characteristics such as their wall (thin, irregular, thickened), septa (thin, irregular, thickened), borders (defined or not) and size, classifications such as Bosniak's classification shown in Table 1 are used, which classifies renal cysts into five categories based on the appearance of the image, to help predict whether it is a benign or malignant tumor
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
A non-invasive diagnostic system for early assessment of acute renal transplant rejection.
Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool
Combining Shape and Learning for Medical Image Analysis
Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields
Biomedical Image Processing and Classification
Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views
Abnormal spleen enlargement (splenomegaly) is regarded as a clinical
indicator for a range of conditions, including liver disease, cancer and blood
diseases. While spleen length measured from ultrasound images is a commonly
used surrogate for spleen size, spleen volume remains the gold standard metric
for assessing splenomegaly and the severity of related clinical conditions.
Computed tomography is the main imaging modality for measuring spleen volume,
but it is less accessible in areas where there is a high prevalence of
splenomegaly (e.g., the Global South). Our objective was to enable automated
spleen volume measurement from 2D cross-sectional segmentations, which can be
obtained from ultrasound imaging. In this study, we describe a variational
autoencoder-based framework to measure spleen volume from single- or dual-view
2D spleen segmentations. We propose and evaluate three volume estimation
methods within this framework. We also demonstrate how 95% confidence intervals
of volume estimates can be produced to make our method more clinically useful.
Our best model achieved mean relative volume accuracies of 86.62% and 92.58%
for single- and dual-view segmentations, respectively, surpassing the
performance of the clinical standard approach of linear regression using manual
measurements and a comparative deep learning-based 2D-3D reconstruction-based
approach. The proposed spleen volume estimation framework can be integrated
into standard clinical workflows which currently use 2D ultrasound images to
measure spleen length. To the best of our knowledge, this is the first work to
achieve direct 3D spleen volume estimation from 2D spleen segmentations.Comment: 22 pages, 7 figure
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