Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …

Objective: Mammogram-based automatic breast cancer detection has a primary role in
accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is …

Purpose For the cancer in the head and neck (HaN), radiotherapy (RT) represents an
important treatment modality. Segmentation of organs‐at‐risk (OARs) is the starting point of …

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative
research. A major limiting factor is the lack of labeled data, as obtaining expert annotations …

Medical image segmentation faces current challenges in effectively extracting and fusing
long-distance and local semantic information, as well as mitigating or eliminating semantic …

Simple Summary Automatic segmentation of organs and other regions of interest is a
promising approach for reducing the workload of doctors in radiotherapeutic planning, but it …

This review article offers a comprehensive analysis of current developments in the
application of machine learning for cancer diagnostic systems. The effectiveness of machine …

Accurate 3D medical imaging can effectively assist doctors in diagnosing diseases.
Currently, deep learning-based segmentation methods have yielded good results but face …

Abstract The Segment Anything Model (SAM) has garnered significant attention for its
versatile segmentation abilities and intuitive prompt-based interface. However its application …

Medical image segmentation is crucial in accurately identifying and delineating regions of
interest in medical images, which can inform the diagnosis and treatment of various …