Addressing Imbalanced Data Challenges in Blood Vessel Image Segmentation: A Comprehensive Review

Abstract

Blood vessel image segmentation is critical for accurate diagnosis and treatment planning in medical imaging, encompassing coronary angiography, cerebral vessel analysis, and peripheral vessel assessment. However, segmentation models face substantial challenges due to imbalanced data, including class imbalance, inter-class imbalance, and the imbalance between vessel regions and background. These disparities undermine segmentation accuracy and pose significant obstacles in clinical applications. This review comprehensively examines these imbalances, discussing their impact on segmentation performance across diverse anatomical regions. Strategies such as enhancing class representation, employing multi-task learning approaches, and utilizing loss weight mapping techniques are explored for their effectiveness in mitigating these challenges. These approaches aim to improve model stability, enhance accuracy in segmenting vessels of varying thickness, and address complexities arising from diverse vessel characteristics and anatomical structures. The discussion emphasizes the necessity for tailored strategies to achieve reliable blood vessel segmentation, crucial for precise disease assessment and treatment planning. By addressing these imbalances effectively, this review contributes to advancing the capabilities of medical image segmentation, ultimately benefiting clinical practice and patient outcomes.

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