3,793 research outputs found
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI
Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor.
Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery.
Deep-learning techniques are becoming popular in medical applications and image-based diagnosis.
Convolutional Neural Networks are the preferred architecture for object detection and classification in images.
In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma
detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing
the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using
Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis,
surgical planning and treatment assessment of brain tumors. Deep convolutional
neural networks (CNNs) have been widely used for this task. Due to the
relatively small data set for training, data augmentation at training time has
been commonly used for better performance of CNNs. Recent works also
demonstrated the usefulness of using augmentation at test time, in addition to
training time, for achieving more robust predictions. We investigate how
test-time augmentation can improve CNNs' performance for brain tumor
segmentation. We used different underpinning network structures and augmented
the image by 3D rotation, flipping, scaling and adding random noise at both
training and test time. Experiments with BraTS 2018 training and validation set
show that test-time augmentation helps to improve the brain tumor segmentation
accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201
A review on a deep learning perspective in brain cancer classification
AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence of three binary segmentation problems according to the subregion
hierarchy. The whole tumor is segmented in the first step and the bounding box
of the result is used for the tumor core segmentation in the second step. The
enhancing tumor core is then segmented based on the bounding box of the tumor
core segmentation result. Our networks consist of multiple layers of
anisotropic and dilated convolution filters, and they are combined with
multi-view fusion to reduce false positives. Residual connections and
multi-scale predictions are employed in these networks to boost the
segmentation performance. Experiments with BraTS 2017 validation set show that
the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for
enhancing tumor core, whole tumor and tumor core, respectively. The
corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and
0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201
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