通过稀疏特征锚点提升前列腺癌病灶分割准确性
MyGO: Make your Goals Obvious, Avoiding Semantic Confusion in Prostate Cancer Lesion Region Segmentation
- 引入像素锚点模块，捕捉全局上下文信息
- 在PI-CAI数据集上达69.73% IoU和74.32% Dice
- 适合需要精准病灶定位的医学图像分析场景
前列腺癌早期诊断与病灶位置及进展的准确识别对制定有效治疗策略至关重要。然而，由于病灶与非病灶区域语义高度相似，现有医学图像分割方法常因语义混淆而难以准确理解病灶语义。为此，我们提出一种新型像素锚点模块，引导模型发现一组稀疏的特征锚点，用于捕获并解析全局上下文信息，增强模型非线性表达能力，提升病灶区域分割精度。此外，设计基于自注意力的Top_k选择策略进一步优化特征锚点识别，并引入焦点损失函数缓解类别不平衡问题，促进不同区域的精确语义解析。本方法在PI-CAI数据集上取得当前最优性能，达到69.73% IoU与74.32% Dice分数，显著提升前列腺癌病灶检测效果。
原文摘要 · Abstract (English)
Early diagnosis and accurate identification of lesion location and progression in prostate cancer (PCa) are critical for assisting clinicians in formulating effective treatment strategies. However, due to the high semantic homogeneity between lesion and non-lesion areas, existing medical image segmentation methods often struggle to accurately comprehend lesion semantics, resulting in the problem of semantic confusion. To address this challenge, we propose a novel Pixel Anchor Module, which guides the model to discover a sparse set of feature anchors that serve to capture and interpret global contextual information. This mechanism enhances the model's nonlinear representation capacity and improves segmentation accuracy within lesion regions. Moreover, we design a self-attention-based Top_k selection strategy to further refine the identification of these feature anchors, and incorporate a focal loss function to mitigate class imbalance, thereby facilitating more precise semantic interpretation across diverse regions. Our method achieves state-of-the-art performance on the PI-CAI dataset, demonstrating 69.73% IoU and 74.32% Dice scores, and significantly improving prostate cancer lesion detection.
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