For the majority of the learning-based segmentation methods, a large quantity of high-quality
training data is required. In this paper, we present a novel learning-based segmentation …

In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation),
a fully unsupervised deep learning framework for medical image segmentation to better …

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

Bone segmentation is essential in Computed Tomography (CT) imaging, which assists
physicians in diagnosing, planning operations, and evaluating treatment effects. A recent …

Medical image segmentation based on deep learning technics has been more and more
prevalent in recent years. The primary reasons lead to success of those methods are radical …

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

This paper presents a novel unsupervised segmentation method for 3D medical images.
Convolutional neural networks (CNNs) have brought significant advances in image …

Deep learning based image segmentation has achieved the state-of-the-art performance in
many medical applications such as lesion quantification, organ detection, etc. However …

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

Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …