  Application of deep learning on histopathological whole slide images (WSIs)
holds promise of improving diagnostic efficiency and reproducibility but is
largely dependent on the ability to write computer code or purchase commercial
solutions. We present a code-free pipeline utilizing free-to-use, open-source
software (QuPath, DeepMIB, and FastPathology) for creating and deploying deep
learning-based segmentation models for computational pathology. We demonstrate
the pipeline on a use case of separating epithelium from stroma in colonic
mucosa. A dataset of 251 annotated WSIs, comprising 140 hematoxylin-eosin
(HE)-stained and 111 CD3 immunostained colon biopsy WSIs, were developed
through active learning using the pipeline. On a hold-out test set of 36 HE and
21 CD3-stained WSIs a mean intersection over union score of 96.6% and 95.3% was
achieved on epithelium segmentation. We demonstrate pathologist-level
segmentation accuracy and clinical acceptable runtime performance and show that
pathologists without programming experience can create near state-of-the-art
segmentation solutions for histopathological WSIs using only free-to-use
software. The study further demonstrates the strength of open-source solutions
in its ability to create generalizable, open pipelines, of which trained models
and predictions can seamlessly be exported in open formats and thereby used in
external solutions. All scripts, trained models, a video tutorial, and the full
dataset of 251 WSIs with ~31k epithelium annotations are made openly available
at https://github.com/andreped/NoCodeSeg to accelerate research in the field.
