Medical image segmentation is crucial for precise diagnosis, treatment planning, and
disease monitoring in healthcare. Traditional methods often struggle with the complexity and …

The segmentation of the orbit in computed tomography (CT) images plays a crucial role in
facilitating the quantitative analysis of orbital decompression surgery for patients with …

Orbital volume assessment is crucial for surgical planning. Traditional methods lack
efficiency and accuracy. Recent studies explore AI-driven techniques, but research on their …

The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess
radiotherapy (RT) planning. Segmentation processes ensure precise target position and …

Introduction This paper presents a novel approach to automate the segmentation of Organ-
at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined …

Thyroid-associated orbitopathy is an autoimmune disease that causes changes in various
structures close to the eye. Medical images, such as three-dimensional computed …

Radiotherapy (RT) is one of the main treatment modalities using high-energy radiation to
eliminate cancer cells in Head and Neck cancer (HNC) for patients worldwide. Advances …

Chest X-rays are commonly used in clinical settings to diagnose thoracic diseases, es-
pecially in low-resource settings. However, interpreting these images can be challeng-ing …

Medical specialty records and imaging knowledge are two types of medical expertise
combined in medical image processing. Imaging modalities generate pixels that depict an …