Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting
various anatomical structures in medical images but often suffer from relatively poor …

For semantic segmentation, label probabilities are often uncalibrated as they are typically
only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score are …

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the
intensity similarity between target and atlas images. However, such similarity can be …

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation.
Generally, MAS methods register multiple atlases, ie, medical images with corresponding …

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating
the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically …

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied
biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). Estimating …

Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical
images. In this work, we improve the performance of MAS by correcting registration errors …

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied
biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical …

Deep learning (DL) is the state-of-the-art methodology in various medical image
segmentation tasks. However, it requires relatively large amounts of manually labeled …