Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

Glioblastomata are the most generally perceived fundamental brain malignant tumors
known as Gliomas, with different shape, size & sub regions. It is hard to segment all three …

Deep neural networks have achieved promising results in a breadth of medical image
segmentation tasks. Nevertheless, they require large training datasets with pixel-wise …

Despite achieving promising results in a breadth of medical image segmentation tasks, deep
neural networks (DNNs) require large training datasets with pixel-wise annotations …

Deep learning based medical image segmentation models usually require large datasets
with high-quality dense segmentations to train, which are very time-consuming and …

Automatic segmentation of the brain Magnetic Resonance Imaging (MRI) plays a crucial role
in many brain MRI processing algorithms, which is effective for the prevention, detection …

Manual delineation of anatomy on existing images is the basis of developing deep learning
algorithms for medical image segmentation. However, manual segmentation is tedious. It is …

Deep neural networks for image quality enhancement typically need large quantities of
highly-curated training data comprising pairs of low-quality images and their corresponding …

Liver and tumor segmentation from abdominal CT scans and an important step towards
computer-assisted diagnosis or treatment planning for various hepatic diseases. Training …

In medical image segmentation tasks, deep learning-based models usually require densely
and precisely annotated datasets to train, which are time-consuming and expensive to …