Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking

TL;DR

This paper introduces Faster Mean-shift, a GPU-accelerated clustering algorithm with an adaptive seed policy, significantly speeding up embedding-based cell segmentation and tracking while maintaining high accuracy.

Contribution

The study presents a novel online seed optimization policy and GPU acceleration for mean-shift, enabling faster and more memory-efficient cell segmentation and tracking.

Findings

Achieved 7-10 times speedup over existing methods.

Highest computational speed among GPU benchmarks for this task.

Demonstrated effectiveness on multiple ISBI cell tracking datasets.

Abstract

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)