  Deep-learning techniques have been used widely to alleviate the
labour-intensive and time-consuming manual annotation required for pixel-level
tissue characterization. Our previous study introduced an efficient single
dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological
segmentation with less computational complexity. However, the patch-wise
segmentation paradigm still applies to Omni-Seg, and the pipeline is
time-consuming when providing segmentation for Whole Slide Images (WSIs). In
this paper, we propose an enhanced version of the Omni-Seg pipeline in order to
reduce the repetitive computing processes and utilize a GPU to accelerate the
model's prediction for both better model performance and faster speed. Our
proposed method's innovative contribution is two-fold: (1) a Docker is released
for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the
pipeline is deployed on a GPU to accelerate the prediction, achieving better
segmentation quality in less time. The proposed accelerated implementation
reduced the average processing time (at the testing stage) on a standard needle
biopsy WSI from 2.3 hours to 22 minutes, using 35 WSIs from the Kidney Tissue
Atlas (KPMP) Datasets. The source code and the Docker have been made publicly
available at https://github.com/ddrrnn123/Omni-Seg.
