用时空模型自动检测血管造影中的血栓，提升卒中手术效率
OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA
- 结合目标检测与时空注意力机制，捕捉动态血管影像特征
- 在真实数据集上实现89.02%精确率和74.87%召回率
- 适合临床辅助诊断与介入手术实时决策场景
急性缺血性卒中患者行血管内血栓切除术时，准确检测血管闭塞至关重要。数字减影血管造影（DSA）序列因解剖结构复杂且时间紧迫，难以精准判读。本文提出OccluNet，一种融合YOLOX与基于Transformer的时空注意力机制的深度学习模型，用于自动化检测DSA序列中的血管闭塞。对比了仅使用单帧图像或最小密度投影训练的YOLOv11基线模型，探索了纯时间注意力与分域时空注意力两种变体。在MR CLEAN注册库的DSA图像上评估显示，该模型能有效捕捉时间一致性特征，达到89.02%的精确率与74.87%的召回率，显著优于基线模型，且两种注意力机制表现相当。源代码已公开于https://github.com/anushka-kore/OccluNet.git。
原文摘要 · Abstract (English)
Accurate detection of vascular occlusions during endovascular thrombectomy (EVT) is critical in acute ischemic stroke (AIS). Interpretation of digital subtraction angiography (DSA) sequences poses challenges due to anatomical complexity and time constraints. This work proposes OccluNet, a spatio-temporal deep learning model that integrates YOLOX, a single-stage object detector, with transformer-based temporal attention mechanisms to automate occlusion detection in DSA sequences. We compared OccluNet with a YOLOv11 baseline trained on either individual DSA frames or minimum intensity projections. Two spatio-temporal variants were explored for OccluNet: pure temporal attention and divided space-time attention. Evaluation on DSA images from the MR CLEAN Registry revealed the model's capability to capture temporally consistent features, achieving precision and recall of 89.02% and 74.87%, respectively. OccluNet significantly outperformed the baseline models, and both attention variants attained similar performance. Source code is available at https://github.com/anushka-kore/OccluNet.git
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