14,061 research outputs found
MELAGE: A purely python based Neuroimaging software (Neonatal)
MELAGE, a pioneering Python-based neuroimaging software, emerges as a
versatile tool for the visualization, processing, and analysis of medical
images. Initially conceived to address the unique challenges of processing 3D
ultrasound and MRI brain images during the neonatal period, MELAGE exhibits
remarkable adaptability, extending its utility to the domain of adult human
brain imaging. At its core, MELAGE features a semi-automatic brain extraction
tool empowered by a deep learning module, ensuring precise and efficient brain
structure extraction from MRI and 3D Ultrasound data. Moreover, MELAGE offers a
comprehensive suite of features, encompassing dynamic 3D visualization,
accurate measurements, and interactive image segmentation. This transformative
software holds immense promise for researchers and clinicians, offering
streamlined image analysis, seamless integration with deep learning algorithms,
and broad applicability in the realm of medical imaging
Pycortex: an interactive surface visualizer for fMRI.
Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software
DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images
Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Machine learning-based imaging diagnostics has recently reached or even
superseded the level of clinical experts in several clinical domains. However,
classification decisions of a trained machine learning system are typically
non-transparent, a major hindrance for clinical integration, error tracking or
knowledge discovery. In this study, we present a transparent deep learning
framework relying on convolutional neural networks (CNNs) and layer-wise
relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is
commonly diagnosed utilizing a combination of clinical presentation and
conventional magnetic resonance imaging (MRI), specifically the occurrence and
presentation of white matter lesions in T2-weighted images. We hypothesized
that using LRP in a naive predictive model would enable us to uncover relevant
image features that a trained CNN uses for decision-making. Since imaging
markers in MS are well-established this would enable us to validate the
respective CNN model. First, we pre-trained a CNN on MRI data from the
Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing
the CNN to discriminate between MS patients and healthy controls (n = 147).
Using LRP, we then produced a heatmap for each subject in the holdout set
depicting the voxel-wise relevance for a particular classification decision.
The resulting CNN model resulted in a balanced accuracy of 87.04% and an area
under the curve of 96.08% in a receiver operating characteristic curve. The
subsequent LRP visualization revealed that the CNN model focuses indeed on
individual lesions, but also incorporates additional information such as lesion
location, non-lesional white matter or gray matter areas such as the thalamus,
which are established conventional and advanced MRI markers in MS. We conclude
that LRP and the proposed framework have the capability to make diagnostic
decisions of..
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
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
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