  In computational digital pathology, accurate nuclear segmentation of
Hematoxylin and Eosin (H&E) stained whole slide images (WSIs) is a critical
step for many analyses and tissue characterizations. One popular deep
learning-based nuclear segmentation approach, HoverNet, offers remarkably
accurate results but lacks the high-throughput performance needed for clinical
deployment in resource-constrained settings. Our approach, HoverFast, aims to
provide fast and accurate nuclear segmentation in H&E images using knowledge
distillation from HoverNet. By redesigning the tool with software engineering
best practices, HoverFast introduces advanced parallel processing capabilities,
efficient data handling, and optimized postprocessing. These improvements
facilitate scalable high-throughput performance, making HoverFast more suitable
for real-time analysis and application in resource-limited environments. Using
a consumer grade Nvidia A5000 GPU, HoverFast showed a 21x speed improvement as
compared to HoverNet; reducing mean analysis time for 40x WSIs from ~2 hours to
6 minutes while retaining a concordant mean Dice score of 0.91 against the
original HoverNet output. Peak memory usage was also reduced 71% from 44.4GB,
to 12.8GB, without requiring SSD-based caching. To ease adoption in research
and clinical contexts, HoverFast aligns with best-practices in terms of (a)
installation, and (b) containerization, while (c) providing outputs compatible
with existing popular open-source image viewing tools such as QuPath. HoverFast
has been made open-source and is available at
andrewjanowczyk.com/open-source-tools/hoverfast.
