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 medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep
up with the ever growing volume of data. While current literature focuses on fully-supervised …

Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis,
treatment planning and following-up. Collecting and annotating a large-scale dataset is …

Curating a large set of fully annotated training data can be costly, especially for the tasks of
medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in …

Supervised learning-based segmentation methods typically require a large number of
annotated training data to generalize well at test time. In medical applications, curating such …

Supervised deep learning methods for segmentation require large amounts of labelled
training data, without which they are prone to overfitting, not generalizing well to unseen …

Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had
a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images …

Segmentation is a fundamental process in microscopic cell image analysis. With the advent
of recent advances in deep learning, more accurate and high-throughput cell segmentation …

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to
obtain, particularly in medical imaging, where annotations also require expert knowledge …