Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

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 …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or
roads, is relevant to many fields of research. For such structures, the topology is their most …

It is critical to quantitatively analyse the developing human fetal brain in order to fully
understand neurodevelopment in both normal fetuses and those with congenital disorders …

In this work, we propose to resolve the issue existing in current deep learning based organ
segmentation systems that they often produce results that do not capture the overall shape …

Skin lesion segmentation is a fundamental procedure in computer-aided melanoma
diagnosis. However, due to the diverse shape, variable size, blurry boundary, and noise …

Medical image segmentation is a fundamental and critical step in many clinical approaches.
Semi-supervised learning has been widely applied to medical image segmentation tasks …

Simple Summary In soft-tissue sarcoma (STS) patients, the decision for the optimal treatment
modality largely depends on STS size, location, and a pathological measure that assesses …

This study utilized deep learning to classify osteoporosis and predict bone density using
opportunistic CT scans and independently tested the models on data from different hospitals …