Article Preview
TopIntroduction
With the rapid advancement of medical diagnostic technologies, particularly the breakthroughs in artificial intelligence (AI) and computer vision, the capacity of machines to process and interpret medical images has reached an unprecedented level of precision and efficiency. From early imaging modalities such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) to ultrasound, medical images have become indispensable tools for clinical diagnosis, disease localization, treatment planning, and postoperative monitoring. These imaging technologies allow clinicians to observe both anatomical structures and pathological variations in a non-invasive manner, thereby enabling early disease detection and more accurate evaluation of treatment outcomes.
However, as imaging devices have gained sophistication, the volume, dimensionality, and heterogeneity of medical data have also expanded dramatically. Manual interpretation by radiologists is often time-consuming, subjective, and prone to inter-observer variability. The demand for high-throughput and objective analysis has thus accelerated the integration of AI-driven solutions in healthcare. In particular, computer-aided diagnosis (CAD) systems have emerged as essential tools that leverage image analysis and machine learning algorithms to assist radiologists in identifying abnormal patterns and quantifying pathological regions (Yanase et al., 2019; Yeasmin et al., 2024). CAD systems not only enhance diagnostic efficiency and reduce misdiagnosis rates but also alleviate the cognitive burden on physicians by providing consistent, data-driven insights.
Despite these advances, several critical challenges remain in CAD-based medical image analysis. First, acquiring large, high-quality annotated datasets is costly and time-intensive, as expert-level annotation requires significant medical expertise. Second, disease manifestations often vary greatly across imaging modalities, patient populations, and acquisition conditions. For instance, the appearance of lesions in CT differs substantially from that in ultrasound or MRI. Such variability leads to reduced generalizability when a model trained on one dataset is applied to another. Therefore, developing robust and generalizable segmentation frameworks capable of adapting to different imaging modalities is vital for clinical reliability and real-world deployment.
Within this context, semantic segmentation plays a fundamental role in the understanding of medical images. As one of the most crucial tasks in image analysis, semantic segmentation aims to assign a class label to every pixel in an image, thereby providing a detailed map of anatomical structures and pathological regions (Liu et al., 2019; Wu, 2017).This fine-grained localization is indispensable for quantitative medical assessment, such as tumor volume measurement, organ delineation, and lesion progression tracking. Traditional rule-based or handcrafted-feature methods, however, often fail to handle the complex textures, noise, and variability present in real-world medical data. In contrast, deep learning, especially convolutional neural network (CNN) technology, has shown remarkable success in extracting hierarchical and discriminative representations, significantly improving segmentation performance across numerous medical applications (Wang et al., 2022).
Nevertheless, a single segmentation model often struggles to achieve cross-modality generalization. The significant differences in image contrast, spatial resolution, and tissue morphology between modalities, such as CT, MRI, X-ray, and ultrasound, pose severe challenges for model robustness. A model optimized for one imaging domain may not perform effectively in another owing to differences in visual patterns and underlying noise distributions (Szegedy et al., 2016). Thus, constructing a unified, adaptive segmentation framework that maintains strong performance across multiple modalities has become a crucial step toward achieving generalized medical image understanding.