As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …

Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

In many medical image analysis applications, only a limited amount of training data is
available due to the costs of image acquisition and the large manual annotation effort …

Vertebral labelling and segmentation are two fundamental tasks in an automated spine
processing pipeline. Reliable and accurate processing of spine images is expected to …

Keywords: CT, Computer Aided Diagnosis (CAD), Computer Applications-General
(Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks …

With the advent of deep learning algorithms, fully automated radiological image analysis is
within reach. In spine imaging, several atlas-and shape-based as well as deep learning …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Localization and segmentation of vertebral bodies from spine CT volumes are crucial for
pathological diagnosis, surgical planning, and postoperative assessment. However, fully …