Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging
research. The size and scope of the training image datasets needed for successful AI model …

Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in
breast cancer diagnosis since it provides complementary information to other imaging …

Background Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐
and inter‐observer variability. An automated deep learning approach to fast and accurate …

Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To
develop a deep-learning-based tool for accurate and robust auto-segmentation of these …

Purpose Accurate deformable registration between computed tomography (CT) and cone‐
beam CT (CBCT) images of pancreatic cancer patients treated with high biologically …

Tumour segmentation in medical images (especially 3D tumour segmentation) is highly
challenging due to the possible similarity between tumours and adjacent tissues, occurrence …

Background Fast and accurate multiorgans segmentation from computed tomography (CT)
scans is essential for radiation treatment planning. Self‐attention (SA)‐based deep learning …

Accurate and robust multiorgan abdominal CT segmentation plays a significant role in
numerous clinical applications, such as therapy treatment planning and treatment delivery …