Brain Cancer

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data.

PURPOSE: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small BM are very difficult to detect manually. With the recent development of deep learning technologies, several res earchers have reported promising resu...

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to ...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current s...

A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.

INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor...

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistica...

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction.

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective ...

A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas.

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annota...

Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum.

INTRODUCTION: It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Rad...

The Effect of Salvage Radiation Therapy on Survival, Functional Outcomes, and Quality of Life in Men with Persistent Prostate-specific Antigen After Robot-Assisted Radical Prostatectomy: Which Patient Benefits More?

PURPOSE: The aim of this study was to evaluate the effect of salvage radiation therapy (sRT) on survival, functional outcomes, and quality of life in ...

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these...

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incu...

Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features.

BACKGROUND: A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months bef...

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.

Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximiz...

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumo...

Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning".

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.

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 dete...

A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method.

Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provid...

A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.

Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary tas...

Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT signif...