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

Area of Science:

• Computational Biology
• Deep Learning
• Medical Image Analysis

Background:

• Single-stage embedding-based deep learning excels at cell segmentation and tracking, outperforming traditional methods.
• However, slow inference speeds limit the practical application of these advanced algorithms.
• Distinguishing overlapping cells remains a challenge for existing segmentation and tracking methods.

Purpose of the Study:

• To address the computational bottleneck in single-stage embedding-based cell segmentation and tracking.
• To introduce a novel algorithm that significantly improves inference speed without compromising accuracy.
• To provide a versatile, plug-and-play solution for medical image analysis.

Main Methods:

• Proposed a novel Faster Mean-shift algorithm incorporating an online seed optimization policy (OSOP).
• OSOP adaptively determines minimal seeds, accelerating computation and optimizing GPU memory usage.
• Validated the algorithm using embedding simulations and empirical data from the ISBI cell tracking challenge.

Main Results:

• Achieved a 7-10 times speedup compared to state-of-the-art embedding-based cell instance segmentation and tracking algorithms.
• Demonstrated superior computational speed and optimized memory consumption against GPU benchmarks.
• The Faster Mean-shift algorithm proved effective across four ISBI cell tracking challenge cohorts.

Conclusions:

• The Faster Mean-shift algorithm effectively overcomes the speed limitations of embedding-based cell segmentation and tracking.
• It offers a significant speedup and memory optimization, enhancing the practicality of deep learning in cell tracking.
• This plug-and-play model is broadly applicable to other pixel embedding-based clustering tasks in medical imaging.