Deep learning based synthetic‐CT generation in radiotherapy and PET: a review
Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …
computed tomography (sCT) have received significant research attention as an alternative to …
Key challenges for delivering clinical impact with artificial intelligence
Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …
potential applications being demonstrated across various domains of medicine. However …
[HTML][HTML] Attention gated networks: Learning to leverage salient regions in medical images
We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
[HTML][HTML] Deep learning strategies for ultrasound in pregnancy
PHB Diniz, Y Yin, S Collins - European Medical Journal …, 2020 - ncbi.nlm.nih.gov
Ultrasound is one of the most ubiquitous imaging modalities in clinical practice. It is cheap,
does not require ionizing radiation and can be performed at the bedside, making it the most …
does not require ionizing radiation and can be performed at the bedside, making it the most …
[HTML][HTML] QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results
Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …
Cardiac substructure segmentation with deep learning for improved cardiac sparing
Purpose Radiation dose to cardiac substructures is related to radiation‐induced heart
disease. However, substructures are not considered in radiation therapy planning (RTP) due …
disease. However, substructures are not considered in radiation therapy planning (RTP) due …
Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study
A Fathi Kazerooni, S Arif, R Madhogarhia… - Neuro-Oncology …, 2023 - academic.oup.com
Background Brain tumors are the most common solid tumors and the leading cause of
cancer-related death among all childhood cancers. Tumor segmentation is essential in …
cancer-related death among all childhood cancers. Tumor segmentation is essential in …
[HTML][HTML] A deep learning framework integrating MRI image preprocessing methods for brain tumor segmentation and classification
Glioma grading is critical in treatment planning and prognosis. This study aims to address
this issue through MRI-based classification to develop an accurate model for glioma …
this issue through MRI-based classification to develop an accurate model for glioma …
Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks
Purpose To investigate the use and efficiency of 3‐D deep learning, fully convolutional
networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell …
networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell …
A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and
function as well as heart diagnosis. The estimation of physiological heart parameters for …
function as well as heart diagnosis. The estimation of physiological heart parameters for …