We present a model-agnostic post-processing scheme to improve the boundary quality for
the segmentation result that is generated by any existing segmentation model. Motivated by …

We review and discuss different classes of image segmentation methods. The usefulness of
these methods is illustrated by a number of clinical cases. Segmentation is the process of …

This paper proposes a new and reliable segmentation approach based on a fusion
framework for combining multiple region-based segmentation maps (with any number of …

We introduce a multi-region model for simultaneous segmentation of medical images. In
contrast to many other models, geometric constraints such as inclusion and exclusion …

Segmentation of cardiac anatomical structures in cardiac magnetic resonance images
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …

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 …

For medical image analysis, segmentation models trained on one or several domains lack
generalization ability to unseen domains due to discrepancies between different data …

Deep convolutional neural networks (CNNs) have been widely used for medical image
segmentation. In most studies, only the output layer is exploited to compute the final …

Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their
ability to capture rich semantic features of the image have been used for image …

Magnetic resonance (MR) protocols rely on several sequences to assess pathology and
organ status properly. Despite advances in image analysis, we tend to treat each sequence …