Patient Specific Segmentation

Patient-specific segmentation in medical imaging aims to create highly accurate and adaptable image segmentation models tailored to individual patients, overcoming limitations of general models. Current research focuses on incorporating clinician feedback, leveraging one-shot learning approaches and part-aware prompting mechanisms, and developing privacy-preserving techniques using methods like image mixing. These advancements improve segmentation accuracy and robustness across diverse datasets and imaging modalities, ultimately enhancing the precision and reliability of medical image analysis for diagnosis and treatment planning.