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Found 14 papers, 5 papers with code
Transfer learning from a sparsely annotated dataset of 3D medical images
A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available.
Kidney abnormality segmentation in thorax-abdomen CT scans
Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.
Lumbar spine segmentation in MR images: a dataset and a public benchmark
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods.
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
This paper identifies a variety of components that increase the performance of CNN-based algorithms for COVID-19 grading from CT images.
Random smooth gray value transformations for cross modality learning with gray value invariant networks
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
To meet demands of the increasing interest in quantification of CAC, i. e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed.
Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning
Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD.
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.
Direct and Real-Time Cardiovascular Risk Prediction
The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages.
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
