Is the medical image segmentation problem solved? A survey of current developments and future directions

2025-08-26

AI Summary

Medical image segmentation faces persistent challenges in weak-supervision generalization, cross-modal alignment, few-shot lesion segmentation, and clinical interpretability. Method: This study systematically reviews a decade of deep learning–driven progress through seven technical dimensions: evolution of supervision paradigms; anatomical scope expansion (from organs to lesions); multimodal fusion; foundation model adaptation; uncertainty modeling; agent-based collaboration; and continual learning. We propose an evolutionary framework tracing the shift from deterministic segmentation to probabilistic modeling and from monolithic inference to multi-agent coordination. Contribution/Results: We identify critical bottlenecks hindering clinical translation and release an actively maintained, open-source literature repository—annotated with hierarchical taxonomies and trend visualizations—to serve as a structured knowledge infrastructure, facilitating methodological innovation and real-world deployment.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues, organs, and pathologies across diverse imaging modalities. This progress raises a fundamental question: to what extent have current models overcome persistent challenges, and what gaps remain? In this work, we provide an in-depth review of medical image segmentation, tracing its progress and key developments over the past decade. We examine core principles, including multiscale analysis, attention mechanisms, and the integration of prior knowledge, across the encoder, bottleneck, skip connections, and decoder components of segmentation networks. Our discussion is organized around seven key dimensions: (1) the shift from supervised to semi-/unsupervised learning, (2) the transition from organ segmentation to lesion-focused tasks, (3) advances in multi-modality integration and domain adaptation, (4) the role of foundation models and transfer learning, (5) the move from deterministic to probabilistic segmentation, (6) the progression from 2D to 3D and 4D segmentation, and (7) the trend from model invocation to segmentation agents. Together, these perspectives provide a holistic overview of the trajectory of deep learning-based medical image segmentation and aim to inspire future innovation. To support ongoing research, we maintain a continually updated repository of relevant literature and open-source resources at https://github.com/apple1986/medicalSegReview

Problem

• Assessing if medical image segmentation is solved by current models
• Identifying remaining gaps and challenges in segmentation techniques
• Reviewing key developments and future directions in the field

Innovation

• Deep learning for medical image segmentation
• Multiscale analysis and attention mechanisms
• Integration of prior knowledge in networks