Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI

Accurate MRI-based quantification of abdominal adipose tissue is critical for metabolic risk assessment but is limited by labor-intensive manual segmentation and the extensive labeled-data dependency of deep learning models. We introduce Dynamic Fuzzy-Gaussian Modeling (DynFGM), a fully automated, unsupervised framework for adipose tissue segmentation designed to operate without requiring training data, expert annotations, or anatomical priors. DynFGM was developed and validated on 776 abdominal MRI scans, using a benchmark cohort (n = 20) with expert ground truth segmentations and a large validation cohort (n = 756). The pipeline dynamically adapts its complexity for each MRI slice by using image intensity kurtosis to select the optimal number of tissue clusters. A fuzzy C-means (FCM) algorithm then initializes a Gaussian mixture model (GMM) for segmentation, providing a mathematically interpretable alternative to black-box neural networks. Finally, a radial distance transform with an adaptive cutoff differentiates subcutaneous (SAT) from visceral adipose tissue (VAT). Performance was evaluated against the ground truth using dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). DynFGM achieved strong spatial agreement with expert annotations (mean DSC: 0.94) and high volumetric reliability (ICC: 0.82–0.97), comparable to reported inter-expert variability. The framework reduced mean absolute volumetric error by 92.6% compared to standard FCM (482.2 cm³ vs. 6547.5 cm³). On the large validation cohort (n = 756), the method demonstrated operational stability, producing physiologically plausible adipose distributions with a low technical failure rate (3.0%). Furthermore, the computational throughput averaged 13.6 s per participant on standard CPU (Intel® Core™ i9, 3.0 GHz) hardware. DynFGM provides an interpretable and data-efficient approach for abdominal adipose tissue phenotyping, offering an alternative to supervised deep learning in settings where labeled data are limited or unavailable. By bridging the gap between manual segmentation and labeled-data-dependent AI, this unsupervised framework offers a scalable tool for population-level research and may serve as an automated labeling tool to facilitate future model development.