AIMC Topic: Brain Neoplasms

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Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

BMC medical imaging
BACKGROUND: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit ...

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assess...

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features wit...

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications.

IEEE transactions on pattern analysis and machine intelligence
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques ...

Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. ...

An advanced MRI and MRSI data fusion scheme for enhancing unsupervised brain tumor differentiation.

Computers in biology and medicine
Proton Magnetic Resonance Spectroscopic Imaging (H MRSI) has shown great potential in tumor diagnosis since it provides localized biochemical information discriminating different tissue types, though it typically has low spatial resolution. Magnetic ...

Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like comput...

Virtual wall-based haptic-guided teleoperated surgical robotic system for single-port brain tumor removal surgery.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
This article presents a haptic-guided teleoperation for a tumor removal surgical robotic system, so-called a SIROMAN system. The system was developed in our previous work to make it possible to access tumor tissue, even those that seat deeply inside ...

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

PloS one
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is o...

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

Medical image analysis
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks pro...