Search Results for author:
Found 10 papers, 8 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET
We also demonstrate the usage of synthetic MS lesions generated on healthy images as data augmentation.
Acute and sub-acute stroke lesion segmentation from multimodal MRI
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
Survival prediction using ensemble tumor segmentation and transfer learning
Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task.
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods.
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diagnosing and monitoring neurological diseases.
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works.
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools.
