SpineContextResUNet: 一种计算高效的残差U-Net用于脊柱CT分割
SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation
AI总结：
本文提出SpineContextResUNet，一种高效的3D残差U-Net，用于快速脊柱定位，通过轻量级的上下文块在不牺牲性能的情况下减少了计算资源需求，适用于资源受限环境。
摘要：
自动分割CT扫描中的脊柱是病理评估和手术规划的前提。然而，基于Transformer或大规模集合的方法需要大量GPU资源，限制了在资源受限环境或边缘设备上的临床应用。为此，我们引入了SpineContextResUNet，一种计算高效的3D残差U-Net，用于快速脊柱定位。我们的架构整合了一个轻量级的上下文块，该块使用并行多扩张卷积来捕捉长距离解剖依赖，而无需递归神经网络（RNN）的高延迟或自注意力机制的记忆开销。在两个公开基准测试集VerSe2020和CTSpine1K上的广泛验证显示，我们的模型分别实现了88.17%和88.13%的Dice分数。为了评估在严格硬件限制下的性能，我们将模型与一个缩放后的瓶颈SwinUNETR进行了比较，以匹配我们的~1.7M硬件足迹。尽管受限的Transformer由于在有限数据集中的空间归纳偏置缺乏而遭受严重性能下降，我们的CNN方法成功地保持了高精度。关键的是，重基线如TotalSegmentator由于在商用硬件（Intel Core i5，8GB RAM）上的内存耗尽而失败，而我们的模型在内存限制下执行稳健，使其成为点诊诊断和在Nvidia Jetson Orin Nano等边缘平台部署的可行解决方案。
英文摘要：
Automated segmentation of the vertebral column in Computed Tomography (CT) scans is a prerequisite for pathological assessment and surgical planning. However, state-of-the-art methods, particularly those based on Transformers or large-scale ensembles, demand substantial GPU resources, creating a barrier for clinical adoption in resource-constrained environments or on edge devices. To address this, we introduce SpineContextResUNet, a computationally efficient 3D Residual U-Net designed for rapid spinal localization. Our architecture integrates a lightweight Context Block that employs parallel multi-dilated convolutions to capture long-range anatomical dependencies without the high latency of Recurrent Neural Networks (RNNs) or the memory overhead of Self-Attention mechanisms. Extensive validation on two public benchmarks, VerSe2020 and CTSpine1K, demonstrates that our model achieves a Dice score of 88.17% and 88.13% respectively. To evaluate performance under strict hardware constraints, we compared our model against a bottlenecked SwinUNETR scaled to match our ~1.7M hardware footprint. While the constrained Transformer suffers severe performance degradation due to a lack of spatial inductive biases in a limited-data regime, our CNN-based approach successfully maintains high accuracy. Crucially, heavy baselines like TotalSegmentator fail due to memory exhaustion on commodity hardware (Intel Core i5, 8GB RAM), our model performs robust inference, making it a viable solution for point-of-care diagnostics and deployment on edge platforms like the Nvidia Jetson Orin Nano.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
