Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI

Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging.

PURPOSE Ensemble segmentation methods combine the segmentation results of individual methods into a final one, with the goal of achieving greater robustness and accuracy. The goal of this study was to develop an ensemble segmentation framework for glioblastoma multiforme tumors on single-channel T1w postcontrast magnetic resonance images. METHODS Three base methods were evaluated in the frame...

Confidence-based ensemble for GBM brain tumor segmentation

It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensem...

Brain Tumor Segmentation using CNN and DNN in MRI Images

Brain tumor extraction and its analysis are challenging tasks in Medical image processing because brain image is complicated. Segmentation plays a very important role in the medical image processing .Image segmentation is used to take out the suspicious parts from MRI. In that way MRI (magnetic resonance imaging) has become a useful medical diagnostic tool for the diagnosis of brain. In this pr...

Automatic MRI Prostate Segmentation based CNN-ASM

CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-view CNN

Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and...