Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

Background: Head and neck cancer (HNC) is characterized by complex-shaped tumors and
numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning …

Abstract Background and Objective: Open-source deep learning toolkits are one of the
driving forces for developing medical image segmentation models that are essential for …

Clinical research on smart health has an increasing demand for intelligent and clinic-
oriented medical image computing algorithms and platforms that support various …

Objectives The aim of this study was to find a new loss function to automatically segment
temporal lobes on localized CT images for radiotherapy with more accuracy and a solution …

Background and Purpose Artificial intelligence (AI) is a technique which tries to think like
humans and mimic human behaviors. It has been considered as an alternative in a lot of …

Abstract Background Delineation of Organs‐at‐Risks (OARs) is an important step in
radiotherapy treatment planning. As manual delineation is time‐consuming, labor‐intensive …

To date, the manual segmentation in radiotherapy contouring is featured with time-and effort-
consuming and low efficiency. Therefore, it is imperative to develop novel technology to …

Introduction Clinical research on smart health has an increasing demand for intelligent and
clinic-oriented medical image computing algorithms and platforms that support various …