Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Deep convolutional networks recently made many breakthroughs in medical image
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …

Deep neural networks have been tremendously successful at segmenting objects in images.
However, it has been shown they still have limitations on challenging problems such as the …

Accurate segmentation of critical anatomical structures is at the core of medical image
analysis. The main bottleneck lies in gathering the requisite expert-labeled image …

Accurate segmentation of anatomic structure is an essential task for biomedical image
analysis. Recent popular object contours regression based segmentation methods have …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

Deep learning has successfully been leveraged for medical image segmentation. It employs
convolutional neural networks (CNN) to learn distinctive image features from a defined pixel …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

Deep convolutional neural networks (CNN) proved to be highly accurate to perform
anatomical segmentation of medical images. However, some of the most popular CNN …

Deep learning has shown great potential for automated medical image segmentation to
improve the precision and speed of disease diagnostics. However, the task presents …