INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP res...
OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines a...
Brain tumors are one of the most dangerous diseases that affect human health and maybe result in death. Detection of brain tumors can be made by using biopsy. However, this is an invasive procedure. It is an extremely dangerous procedure because it c...
In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for se...
BACKGROUND: A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in te...
One of the most important resources used in today's world is image. Medical images can play an essential role in helping diagnose diseases. Doctors and specialists use medical images to diagnose brain diseases. Convolution neural networks are among t...
International journal of radiation oncology, biology, physics
Jul 2, 2022
PURPOSE: Deep learning-based algorithms have been shown to be able to automatically detect and segment brain metastases (BMs) in magnetic resonance imaging, mostly based on single-institutional data sets. This work aimed to investigate the use of dee...
IEEE journal of biomedical and health informatics
Jul 1, 2022
Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for th...
Computational and mathematical methods in medicine
Jul 1, 2022
Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can ...
Topics in magnetic resonance imaging : TMRI
Jun 28, 2022
OBJECTIVES: Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remar...
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