TL;DR
This study evaluates 26 methods across nine models and three datasets, analyzing factors such as IoU thresholds, embedding models, feature fusion techniques, and key parameters, and finds that complexity-based and learning-based methods consistently perform best across all settings.
Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deploy...
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
A novel IIS framework based on Multi-Layer Perceptron (MLP), which fuses clicks from users with feature representations extracted from the Transformer Encoder, which closes the gap between interactive user instructions and deep learning and provides a practical and flexible solution for accurate segmentation in many ap...
A 3D deep learning model that classified MR volumes and their automated segmentation outputs into downstream Accept, Reject, and Rework categories could support clinical triage after automated segmentation to focus radiologist effort on flagged cases and expedited data curation in research studies where scan/segmentati...
DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...
Accurate image segmentation remains a challenging task, particularly in complex domains such as medical imaging, where precise boundary detection and feature discrimination are critical. In this work, an attention-enhanced U-Net architecture is proposed to improve segmentation performance by integrating multiple attent...
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