AI Medical Compendium Topic:
Brain Neoplasms

Clear Filters Showing 791 to 800 of 1033 articles

Machine learning based brain tumour segmentation on limited data using local texture and abnormality.

Computers in biology and medicine
Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four differ...

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.

Scientific reports
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and textur...

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

Journal of healthcare engineering
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic ...

Predicting cancer outcomes from histology and genomics using convolutional networks.

Proceedings of the National Academy of Sciences of the United States of America
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digit...

A convolutional neural network to filter artifacts in spectroscopic MRI.

Magnetic resonance in medicine
PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viab...

Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images.

IEEE transactions on medical imaging
Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe ...

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

Computers in biology and medicine
Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection ...

Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

Medical physics
BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs.

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

Microscopy research and technique
A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treat...