FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision

2025-11-14

AI Summary

Medical image segmentation often suffers from blurred boundaries due to insufficient encoding of boundary information. To address this, we propose FocusSDF—a novel adaptive boundary-aware loss function based on the Signed Distance Function (SDF)—which dynamically weights pixels in the neighborhood of lesion/organ boundaries to explicitly guide the network to focus on boundary regions. Its key innovation lies in being the first to incorporate SDF directly into the supervision mechanism, enabling boundary-sensitive end-to-end optimization while maintaining compatibility with multimodal imaging and mainstream segmentation architectures. Evaluated on public datasets covering brain aneurysms, stroke, liver, and breast tumors, FocusSDF consistently outperforms five state-of-the-art distance-transform-based losses. Significant improvements are observed in boundary-specific metrics—including Hausdorff distance and average surface distance—demonstrating its effectiveness in fine-grained boundary modeling.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Segmentation of medical images constitutes an essential component of medical image analysis, providing the foundation for precise diagnosis and efficient therapeutic interventions in clinical practices. Despite substantial progress, most segmentation models do not explicitly encode boundary information; as a result, making boundary preservation a persistent challenge in medical image segmentation. To address this challenge, we introduce FocusSDF, a novel loss function based on the signed distance functions (SDFs), which redirects the network to concentrate on boundary regions by adaptively assigning higher weights to pixels closer to the lesion or organ boundary, effectively making it boundary aware. To rigorously validate FocusSDF, we perform extensive evaluations against five state-of-the-art medical image segmentation models, including the foundation model MedSAM, using four distance-based loss functions across diverse datasets covering cerebral aneurysm, stroke, liver, and breast tumor segmentation tasks spanning multiple imaging modalities. The experimental results consistently demonstrate the superior performance of FocusSDF over existing distance transform based loss functions.

Problem

• Addresses boundary preservation challenges in medical image segmentation
• Introduces boundary-aware loss function using signed distance supervision
• Improves segmentation accuracy across multiple medical imaging modalities

Innovation

• FocusSDF loss function uses signed distance supervision
• Adaptively weights pixels near lesion or organ boundaries
• Enhances boundary-aware medical image segmentation performance