Brain Cancer

Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.

INTRODUCTION: Contouring organs at risk (OARs) is a time-intensive task that is a critical part of radiation therapy. Atlas-based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto-segmentation has recently emerged as a promising ...

Auto-segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning.

BACKGROUND: Routinely delineating of important skeletal growth centers is imperative to mitigate radiation-induced growth abnormalities for pediatric cancer patients treated with radiotherapy. However, it is hindered by several practical problems, including difficult identification, time consumption, and inter-practitioner variability.

Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study.

Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, limiting overall radiation dose reduction potentia...

Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, ...

An Intelligent Robot Detection System of Uncontrolled Radioactive Sources.

In recent years, radioactive sources have been widely used in various fields (e.g., nuclear industry, agriculture, medical industry, environmental pro...

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and tempor...

The three horizons model applied to medical science.

The three horizons model is a framework that helps manage an organization's innovation strategy. This model considers three aspects (horizons) that sh...

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response ...

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with e...

A data augmentation method for fully automatic brain tumor segmentation.

Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an aug...

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolu...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investi...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a ...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brai...

Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.

Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brach...

Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning.

BACKGROUND AND PURPOSE: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may ha...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose di...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always resul...

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis,...

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer va...