Vol. 1 No. 4 (2026)

Articles

Probabilistic Medical Image Segmentation with Calibrated Uncertainty Estimation across Heterogeneous Clinical Images, Variable Acquisition Conditions, and Anatomies

Published 2026-08-07

  • Victor Yiu

Victor Yiu
Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

Artificial Intelligence and Intelligent Systems Research, Volume 1, Issue 4, 2026 cover

Abstract

Medical image segmentation is a foundational task in computational pathology and radiology, facilitating precise anatomical localization, volumetric analysis, and downstream treatment planning. Despite the paradigm-shifting success of deep learning architectures in these domains, standard deterministic models remain notoriously overconfident and fail to provide reliable uncertainty estimates, particularly when deployed in heterogeneous clinical environments. This paper presents a comprehensive investigation into probabilistic medical image segmentation, emphasizing the critical requirement of calibrated uncertainty estimation across a wide spectrum of imaging modalities, variable acquisition conditions, and diverse anatomical structures. We systematically explore the theoretical underpinnings of Bayesian neural networks, stochastic latent variable models, and ensemble methodologies tailored for dense prediction tasks. Furthermore, we introduce robust calibration mechanisms designed to align the confidence of predictive models with their empirical accuracy, mitigating the severe risks associated with miscalibrated overconfidence in life-critical diagnostic systems. Through extensive theoretical analysis and empirical evaluation across multiple clinical datasets, encompassing neuro-oncology, hepatology, and cardiology, this research demonstrates that appropriately calibrated probabilistic frameworks not only yield competitive segmentation fidelity but also produce highly interpretable uncertainty maps. These uncertainty maps effectively capture both aleatoric and epistemic uncertainties, serving as vital signals for human-in-the-loop clinical workflows. The integration of advanced calibration techniques ensures that the segmentation models maintain diagnostic reliability even under significant domain shifts caused by disparate scanner protocols and inherent physiological variability.

Keywords

Medical Image Segmentation, Probabilistic Modeling, Uncertainty Calibration, Clinical Heterogeneity

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