Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking (2007.14283v2)

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)

• The paper introduces a novel GPU-accelerated Faster Mean-shift algorithm that achieves a 7-10 fold increase in clustering speed for cell segmentation and tracking.
• The method employs an Online Seed Optimization Policy and an early stopping strategy to dynamically minimize seed usage and reduce computational load.
• Empirical tests on ISBI challenge datasets validate its efficiency, paving the way for real-time, high-throughput cell screening applications.

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

The paper presents an enhancement on embedding-based cell segmentation and tracking methodologies by introducing the "Faster Mean-shift" algorithm. Building on the existing architectures, notably Payer et al.'s cosine embedding-based recurrent stacked hourglass network (RSHN), this work addresses the computational bottleneck that arises primarily in the clustering phase of these models.

Core Innovations

The paper proposes an innovative GPU-accelerated clustering method designed to overcome inefficiencies observed in cost and performance when employing traditional mean-shift algorithms in pixel embedding contexts. The main innovations include:

1. Online Seed Optimization Policy (OSOP): This policy dynamically determines the minimal number of seeds required, optimizing both computational speed and GPU memory usage.
2. Early Stopping Strategy: It reduces unnecessary computation, further contributing to computational efficiency by halting iterations once a sufficient percentage of cluster seeds have converged.
3. GPU-based Parallelization: By utilizing GPU resources, Faster Mean-shift performs the mean-shift iterations for seeds concurrently, significantly enhancing processing speeds. This approach is a departure from the memory-intensive strategies of previous fast mean-shift algorithms.

Numerical Evaluation

Empirical testing shows the Faster Mean-shift algorithm achieves a 7-10 fold increase in processing speed over the traditional approaches without sacrificing accuracy. This performance gains credibility through evaluations on the ISBI cell tracking challenge data sets, where Faster Mean-shift reveals superior computational efficiency and optimized memory consumption even in large data contexts.

Implications and Future Directions

The implications of this work are significant for real-time image analysis in medical domains where processing large volumes of microscopy images is routine. The efficiency improvements facilitate the deployment of embedding-based methods in real-world applications that require rapid analysis, like high-throughput cell screening.

Future research could explore extending the Faster Mean-shift algorithm to three-dimensional data, an avenue increasingly relevant with advancements in 3D microscopy. Additionally, the algorithm could be refined to better handle images of varying resolutions, optimizing its application across diverse datasets.

In conclusion, Faster Mean-shift represents a crucial step forward in embedding-based clustering algorithms, with implications that span beyond cell segmentation into broader medical imaging applications. The algorithm's adaptability as a plug-and-play component further underscores its potential utility across various pixel embedding tasks within the computational imaging domain.