Prospective deployment of deep learning in MRI: a framework for important considerations, challenges, and recommendations for best practices
Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net
Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and
its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such …
its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such …
Improving robustness of deep learning based knee mri segmentation: Mixup and adversarial domain adaptation
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA)
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …
Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However,
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …
The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset
Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …
Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers
AS Chaudhari, KJ Stevens, JP Wood… - Journal of Magnetic …, 2020 - Wiley Online Library
Background Super‐resolution is an emerging method for enhancing MRI resolution;
however, its impact on image quality is still unknown. Purpose To evaluate MRI super …
however, its impact on image quality is still unknown. Purpose To evaluate MRI super …
Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial …
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD)
events—the leading cause of global mortality—have known limitations and may be …
events—the leading cause of global mortality—have known limitations and may be …
Fire segmentation using a DeepLabv3+ architecture
In the last decade, the number of forest fires events is growing due to the fast change of
earth's climate. Hence, more automatized fire fighting actions had become necessary. Deep …
earth's climate. Hence, more automatized fire fighting actions had become necessary. Deep …
Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI …
Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of
quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T …
quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T …
Improving data-efficiency and robustness of medical imaging segmentation using inpainting-based self-supervised learning
We systematically evaluate the training methodology and efficacy of two inpainting-based
pretext tasks of context prediction and context restoration for medical image segmentation …
pretext tasks of context prediction and context restoration for medical image segmentation …