更快的Mean-shift：GPU加速聚类，用于基于余弦嵌入的细胞分割和跟踪
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
摘要：
近年来，基于单阶段嵌入的深度学习算法在细胞分割和跟踪中获得了越来越多的关注。与传统的“分割-关联”两阶段算法相比，单阶段算法不仅能够同时实现一致的实例细胞分割和跟踪，而且在区分边界和重叠区域上的模糊像素时具有更好的性能。然而，基于嵌入的算法的部署受到慢推理速度的限制（例如，每帧大约1-2分钟）。在这项研究中，我们提出了一种新的快速均值漂移算法，它解决了基于嵌入的细胞分割和跟踪的计算瓶颈。与以往GPU加速的快速均值漂移算法不同，提出了一种新的在线种子优化策略（OSOP），自适应地确定最小种子数，加快计算速度，节省GPU内存。通过ISBI细胞跟踪挑战的四个队列的嵌入模拟和经验验证，与最先进的基于嵌入的细胞实例分割和跟踪算法相比，所提出的更快的均值漂移算法实现了7-10倍的加速。与其他GPU基准测试相比，我们的Faster Mean-shift算法还实现了最高的计算速度，并优化了内存消耗。Faster Mean-shift算法是一种即插即用的模型，可用于其他基于像素嵌入的医学图像聚类分析。（即插即用模型已公开：https：//github.com/masqm/Faster-Mean-Shift）
英文摘要：
Recently, single-stage embedding based deep learning algorithms gain increasing attention in cell segmentation and tracking. Compared with the traditional "segment-then-associate" two-stage approach, a single-stage algorithm not only simultaneously achieves consistent instance cell segmentation and tracking but also gains superior performance when distinguishing ambiguous pixels on boundaries and overlaps. However, the deployment of an embedding based algorithm is restricted by slow inference speed (e.g., around 1-2 mins per frame). In this study, we propose a novel Faster Mean-shift algorithm, which tackles the computational bottleneck of embedding based cell segmentation and tracking. Different from previous GPU-accelerated fast mean-shift algorithms, a new online seed optimization policy (OSOP) is introduced to adaptively determine the minimal number of seeds, accelerate computation, and save GPU memory. With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm. Our Faster Mean-shift algorithm also achieved the highest computational speed compared to other GPU benchmarks with optimized memory consumption. The Faster Mean-shift is a plug-and-play model, which can be employed on other pixel embedding based clustering inference for medical image analysis. (Plug-and-play model is publicly available: https://github.com/masqm/Faster-Mean-Shift)
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。
