Recent Advances in Medical Imaging Segmentation: A Survey

TL;DR

This survey reviews recent advances in medical imaging segmentation, focusing on innovative methods like Generative AI, Few-Shot Learning, and Foundation Models to address longstanding challenges and improve model robustness and accessibility.

Contribution

It provides a comprehensive overview of cutting-edge methodologies, theoretical foundations, and recent applications, highlighting future directions for practical and accessible segmentation models.

Findings

Generative AI and Foundation Models show promise in medical segmentation.

Recent methods improve robustness but face domain adaptation challenges.

The survey identifies key limitations and future research avenues.

Abstract

Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey explores cutting-edge advancements in medical image segmentation, focusing on methodologies such as Generative AI, Few-Shot Learning, Foundation Models, and Universal Models. These approaches offer promising solutions to longstanding challenges. We provide a comprehensive overview of the theoretical foundations, state-of-the-art techniques, and recent applications of these methods. Finally, we discuss inherent limitations, unresolved issues, and future research directions aimed at enhancing the practicality and accessibility of segmentation models in medical imaging. We are maintaining a \href{https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation}{GitHub Repository} to continue tracking and updating innovations in this field.