  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)
