A Unified Framework for the Detection and Classification of Fatty Pancreas in Ultrasound Images

TL;DR

This paper introduces an end-to-end automated framework using segmentation and texture analysis to classify fatty pancreas in ultrasound images, achieving high accuracy and interpretability.

Contribution

The study presents the first automated pipeline combining segmentation and texture comparison for fatty pancreas detection in ultrasound images.

Findings

Achieved 89.7% accuracy with SVM classifier.

Demonstrated the effectiveness of segmentation-guided texture features.

Validated on a clinical dataset of 214 ultrasound images.

Abstract

Non-alcoholic fatty pancreas disease (NAFPD) is an underdiagnosed condition associated with metabolic syndrome, insulin resistance, and increased risk of pancreatic cancer. Diagnosis typically relies on subjective visual assessment of ultrasound images by clinicians. We propose an end-to-end framework for automatically classifying normal versus fatty pancreas from abdominal ultrasound images. Our method employs a TransUNet-based segmentation architecture with a ResNet encoder and transformer bottleneck to delineate the pancreas and the splenic vein, followed by anatomically-guided patch extraction and patient-level classification through pairwise texture comparison. The feature engineering mimics clinical reasoning by comparing the echogenicity of peri-venous fat to the pancreatic parenchyma, providing an interpretable signal for classification. The segmentation models are initialized via domain-specific transfer learning from a liver segmentation task. We validate the full pipeline on a clinical dataset of 214 abdominal ultrasound images with 107 expert-labeled cases using 5-fold cross-validation. SVM with RBF kernel achieves a mean cross-validated accuracy of 89.7\%\,$\pm$\,1.8\% and F1 of 0.898\,$\pm$\,0.019, while the unsupervised K-Means baseline reaches 87.8\% accuracy, demonstrating that the proposed features capture the relevant clinical signal even without labeled training data. To our knowledge, this is the first end-to-end automated framework for fatty pancreas classification from ultrasound using segmentation-guided texture analysis.