Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

Big medical data mainly include electronic health record data, medical image data, gene
information data, etc. Among them, medical image data account for the vast majority of …

The U-Net was presented in 2015. With its straight-forward and successful architecture it
quickly evolved to a commonly used benchmark in medical image segmentation. The …

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the
BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are …

Image segmentation is an important step in medical image processing and has been widely
studied and developed for refinement of clinical analysis and applications. New models …

We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …