Recent Advances in Medical Imaging Segmentation: A Survey

2025-05-14

AI Summary

Medical image segmentation faces fundamental challenges including data scarcity, high annotation costs, poor cross-modal and cross-domain generalizability, and stringent privacy constraints. To address these, this work systematically reviews over 200 state-of-the-art publications and—uniquely—integrates perspectives from generative AI (e.g., diffusion models) and foundation models into a clinically oriented evaluation framework. We delineate adaptation pathways for foundation models in medical segmentation, identify critical bottlenecks (e.g., domain misalignment, computational overhead), and propose lightweight fine-tuning strategies—including visual prompting, multimodal fusion, and self-supervised pretraining. Our contributions include a continuously updated, open-source knowledge repository on GitHub, featuring a structured technical roadmap that bridges algorithmic innovation with clinical translation. This resource supports both methodological development and real-world deployment of segmentation models in healthcare settings.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Addressing challenges in medical image segmentation due to data variability
• Improving generalization and domain adaptation in segmentation models
• Exploring advanced methods like Generative AI for segmentation solutions

Innovation

• Generative AI for medical image segmentation
• Few-Shot Learning to reduce annotation complexity
• Foundation Models for robust generalization