A Two-Stage CNN-Based Method for Enhanced Metastasis Segmentation in SPECT Bone Scans

Abstract

Accurate segmentation of metastatic lesions is crucial for improving the quality of patient care, particularly in the context of bone scans. However, existing automated methods, which are predominantly data-driven, exhibit limited performance and lack interpretability. To address these challenges, we propose a novel two-stage framework that integrates human domain knowledge with data patterns to enhance CNN-based metastasis lesion segmentation in bone scans. The proposed method comprises two phases: Stage I detects hotspots in bone scans using a CNN-based model, while Stage II identifies actual metastases by leveraging clinical knowledge of uptake intensity asymmetry. Our approach incorporates a dual-sampling scheme inspired by diagnostic patterns and an enhanced feature extractor within the hotspot segmentation network, thus augmenting the detection capabilities of traditional data-driven CNN models. The assessment of symmetrical uptake intensity starts with the symmetry axis of the trunk in the image, followed by a composite similarity measure that considers both geometric symmetry and intensity consistency. Experimental evaluations on 302 clinical cases reveal that our proposed segmentation network improves the Dice similarity coefficient score by 4.34% compared to the baseline method. Furthermore, integrating clinical knowledge significantly reduces false positives, improving the class pixel accuracy score by 2.39% and demonstrating notable adaptability to other segmentation models. Comparative analysis with existing models for metastasis lesion segmentation demonstrates the superior performance of our approach. By incorporating domain knowledge into data patterns, our method enhances automated segmentation performance and bridges the gap between domain expertise and data-driven methodologies in the automated analysis of low-resolution bone scans.