构建脊柱病理诊断标准数据集，发现矢状T2序列最有效
PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis
- 用位置编码建模椎间盘解剖上下文，提升诊断精度
- 矢状T2序列宏平均F1达50.31%，优于多序列融合策略
- 揭示多序列融合因噪声干扰反而降低性能，适合研究者参考
脊柱病理的准确诊断依赖影像学解读，但自动化系统受限于缺乏多样且高质量的基准。本研究提出PhenSPINE，一个包含250名患者共16,813张MRI图像的数据集，旨在推动深度学习研究。我们设计了一个鲁棒的诊断基准，结合先进的卷积骨干网络与位置编码机制，显式建模椎间盘的解剖上下文。在四种标准MRI序列上评估，结果表明矢状T2加权序列具有最强诊断价值，宏平均F1分数达50.31%。我们发现，相较于该单序列基线，多序列融合策略表现更差，因数据集中各序列图像受周围解剖结构噪声显著干扰。本工作建立了可靠基线，并为脊柱分析中的序列选择提供了关键洞见。
原文摘要 · Abstract (English)
The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks. In this study, we present PhenSPINE, a Magnetic Resonance Imaging dataset comprising 16,813 images from 250 patients, curated to facilitate advanced deep learning research. We propose a robust diagnostic benchmark that integrates state-of-theart convolutional backbones with a Positional Encoding mechanism to explicitly model the anatomical context of intervertebral discs. Evaluating across four standard MRI sequences, our experiments demonstrate that the Sagittal T2-weighted sequence offers the most robust diagnostic value, achieving a superior Macro F1-score of 50.31%. We find that multisequence fusion strategies yield inferior performance compared to this single-sequence baseline, as the images across sequences in our dataset are significantly compromised by noise interference from surrounding anatomical regions. This work establishes a robust baseline and offers critical insights into sequence selection for spine analysis.
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