Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation

2025-11-21

AI Summary

Small cerebellar lesions are challenging to segment due to their minute volume, leading to suboptimal optimization and frequent false negatives when using conventional voxel-level loss functions (e.g., Dice). To address this, we propose CC-DiceCE, a novel instance-level loss function that integrates CC-Metrics—measuring lesion-level correspondence via connected components—into segmentation loss design for the first time. Implemented and validated within the nnU-Net framework, CC-DiceCE models lesions as connected-component instances, thereby enhancing sensitivity to small pathological structures. Experiments on a multi-center dataset demonstrate that CC-DiceCE significantly improves recall for small lesions (+8.2%–12.7%) over state-of-the-art blob-based losses, while maintaining stable overall Dice scores and incurring only a marginal increase in false positives. This confirms the efficacy and generalizability of instance-level supervision for segmenting tiny anatomical abnormalities.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss. Both are benchmarked against a DiceCE baseline within the nnU-Net framework, which provides a robust and standardized setup. We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, albeit at the cost of slightly more false positives. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.

Problem

• Addresses under-segmentation of small cerebral lesions
• Proposes instance-wise loss to improve lesion detection
• Compares CC-DiceCE with blob loss for segmentation accuracy

Innovation

• CC-DiceCE loss improves small lesion detection
• Instance-wise loss based on CC-Metrics framework
• Outperforms blob loss in multi-dataset evaluation