Abstract
Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). This paper develops a lightweight computer vision framework that reconciles the efficiency of 2D detection with the necessity of 3D context by reformulating volumetric Computer Tomography (CT) data as sequential video streams. This video-viewpoint paradigm is applied to the time-sensitive task of Intracranial Hemorrhage (ICH) detection using the Hemorica dataset. To ensure operational efficiency, we benchmarked multiple generations of the YOLO architecture (v8, v10, v11 and v12) in their Nano configurations, selecting the version with the highest mAP@50 to serve as the slice-level backbone. A ByteTrack algorithm is then introduced to enforce anatomical consistency across the $z$-axis. To address the initialization lag inherent in video trackers, a hybrid inference strategy and a spatiotemporal consistency filter are proposed to distinguish true pathology from transient prediction noise. Experimental results on independent test data demonstrate that the proposed framework serves as a rigorous temporal validator, increasing detection Precision from 0.703 to 0.779 compared to the baseline 2D detector, while maintaining high sensitivity. By approximating 3D contextual reasoning at a fraction of the computational cost, this method provides a scalable solution for real-time patient prioritization in resource-constrained environments, such as mobile stroke units and IoT-enabled remote clinics.
Abstract (translated)
自动化分析边缘设备上的体积医学影像受到三维卷积神经网络（CNN）高内存和计算需求的严重限制。本文开发了一种轻量级计算机视觉框架，通过将体积型计算机断层扫描（CT）数据重新表述为顺序视频流，解决了二维检测效率与三维上下文必要性的矛盾。这一基于视频视角的方法被应用于具有时间敏感性任务的颅内出血（ICH）检测，并使用Hemorica数据集进行验证。 为了确保运行效率，我们在Nano配置下对YOLO架构的不同版本（v8、v10、v11和v12）进行了基准测试，并选择了mAP@50值最高的版本作为二维切片级别的主干网络。随后引入了一种ByteTrack算法，以强制执行沿z轴的解剖学一致性。为了解决视频跟踪器初始化时固有的滞后问题，我们提出了一种混合推理策略和时空一致性过滤机制，用以区分真正的病灶与瞬态预测噪声。 在独立测试数据上的实验结果表明，所提出的框架作为一个严格的时间验证工具，在保持高灵敏度的同时，相较于基准二维检测器将检测精度从0.703提升至0.779。通过大幅降低计算成本来近似三维上下文推理，该方法为资源受限环境中的实时患者优先级划分提供了可扩展的解决方案，例如移动中风单位和物联网远程诊所。
URL
https://arxiv.org/abs/2601.02521
