AIMC Topic: Brain Neoplasms

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F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

NeuroImage. Clinical
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...

Microvascularity detection and quantification in glioma: a novel deep-learning-based framework.

Laboratory investigation; a journal of technical methods and pathology
Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for anti...

Learning image-based spatial transformations via convolutional neural networks: A review.

Magnetic resonance imaging
Recent methodological innovations in deep learning and associated advancements in computational hardware have significantly impacted the various core subfields of quantitative medical image analysis. The generalizability, computational efficiency and...

A review on brain tumor segmentation of MRI images.

Magnetic resonance imaging
The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segment...

DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Journal of medical systems
Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentatio...

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned.

Magnetic resonance imaging
The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a ...

Automated brain histology classification using machine learning.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Brain and breast tumors cause significant morbidity and mortality worldwide. Accurate and expedient histological diagnosis of patients' tumor specimens is required for subsequent treatment and prognostication. Currently, histology slides are visually...

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.

Genomics
BACKGROUND: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mec...

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks.

IEEE transactions on medical imaging
Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatia...

Brain tumor detection using statistical and machine learning method.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumo...