A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
SlicerMorph: An open and extensible platform to retrieve, visualize and analyse 3D morphology
Large‐scale digitization projects such as# ScanAllFishes and oVert are generating high‐
resolution microCT scans of vertebrates by the thousands. Data from these projects are …
resolution microCT scans of vertebrates by the thousands. Data from these projects are …
Generating synthetic labeled data from existing anatomical models: an example with echocardiography segmentation
Deep learning can bring time savings and increased reproducibility to medical image
analysis. However, acquiring training data is challenging due to the time-intensive nature of …
analysis. However, acquiring training data is challenging due to the time-intensive nature of …
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Anatomical segmentation is a fundamental task in medical image computing, generally
tackled with fully convolutional neural networks which produce dense segmentation masks …
tackled with fully convolutional neural networks which produce dense segmentation masks …
Cascaded statistical shape model based segmentation of the full lower limb in CT
EA Audenaert, J Van Houcke, DF Almeida… - Computer methods in …, 2019 - Taylor & Francis
Image segmentation has become an important tool in orthopedic and biomechanical
research. However, it greatly remains a time-consuming and laborious task. In this …
research. However, it greatly remains a time-consuming and laborious task. In this …
Deep implicit statistical shape models for 3d medical image delineation
Abstract 3D delineation of anatomical structures is a cardinal goal in medical imaging
analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical …
analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical …
[HTML][HTML] Atlas-ISTN: joint segmentation, registration and atlas construction with image-and-spatial transformer networks
Deep learning models for semantic segmentation are able to learn powerful representations
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …
DenseNet-based transfer learning for LV shape Classification: Introducing a novel information fusion and data augmentation using statistical Shape/Color modeling
FB Mofrad, G Valizadeh - Expert Systems with Applications, 2023 - Elsevier
Purpose Myocardial infarction (MI) causes the left ventricle (LV) remodeling. Statistical
shape modeling (SSM) can provide valuable and reliable information about the changes in …
shape modeling (SSM) can provide valuable and reliable information about the changes in …
Geo-sic: learning deformable geometric shapes in deep image classifiers
Deformable shapes provide important and complex geometric features of objects presented
in images. However, such information is oftentimes missing or underutilized as implicit …
in images. However, such information is oftentimes missing or underutilized as implicit …