In recent years, significant progress has been made in developing more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck
(H&N), which requires a precise spatial description of the target volumes and organs at risk …

Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a
challenging task in medical image analysis domain. To perform the segmentation, we use …

Accurate segmentation of organs at risk (OARs) plays a critical role in the treatment planning
of image-guided radiotherapy of head and neck cancer. This segmentation task is …

Deep learning (DL)-based auto-segmentation has the potential for accurate organ
delineation in radiotherapy applications but requires large amounts of clean labeled data to …

Purpose In radiotherapy planning, manual contouring is labor‐intensive and time‐
consuming. Accurate and robust automated segmentation models improve the efficiency …

Partly due to the use of exhaustive-annotated data, deep networks have achieved
impressive performance on medical image segmentation. Medical imaging data paired with …

Objective. Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk
(OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No …

Automatic segmentation of organs-at-risk (OARs) of the head and neck, such as the
brainstem, the left and right parotid glands, mandible, optic chiasm, and the left and right …

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of
optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and …