Adapting Prompt-Driven Approach for Diabetic Foot Ulcer Segmentation: A Sri Lankan Patient Study

Abstract

Diabetes, a widespread metabolic disorder affecting millions globally, including many in Sri Lanka, often results in diabetic foot ulcers (DFUs). Accurate wound measurement is vital for diagnosing and monitoring DFUs, providing key metrics to track healing and inform treatment decisions. However, manual measurement is both time-consuming and prone to inaccuracies, often leading to misdiagnoses, and flawed documentation. Automated wound segmentation from medical images presents a promising solution by automating wound area assessment and enhancing patient outcomes. This study proposes a prompt driven segmentation approach to tackle the unique challenges of DFU segmentation, particularly for a limited dataset of Sri Lankan patients. This approach enables the model to use user input like points, regions, or bounding boxes to focus on complex areas, enhancing accuracy in cases with unclear wound boundaries or sparse data. The primary objectives of this research include the creation of a comprehensive dataset reflecting diverse DFU instances in Sri Lanka and the adaptation of the Segment Anything Model (SAM) to accurately segment DFU areas with minimal supervision. The dataset consists of 300 samples from 180 patients at Dambulla Base Hospital, capturing variations in skin tone, ulcer size, shape, and surrounding tissue conditions. The pre-trained SAM was fine-tuned using prompt driven segmentation techniques to handle the unique characteristics of Sri Lankan patients. The model demonstrated strong performance, achieving a Dice Coefficient of 83.31%, Specificity of 99.73%, and Precision of 90.42%. This approach minimizes the need for large annotated datasets, enhances generalisation from limited data, and allows user-guided refinement. The development of population-specific models in this study has the potential to enhance clinical decision-making and significantly improve patient care for DFU management in Sri Lanka.