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Application of deep learning on histopathological whole slide images (WSIs)holds promise of improving diagnostic efficiency and reproducibility but islargely dependent on the ability to write computer code or purchase commercialsolutions. We present a code-free pipeline utilizing free-to-use, open-sourcesoftware (QuPath, DeepMIB, and FastPathology) for creating and deploying deeplearning-based segmentation models for computational pathology. We demonstratethe pipeline on a use case of separating epithelium from stroma in colonicmucosa. A dataset of 251 annotated WSIs, comprising 140 hematoxylin-eosin(HE)-stained and 111 CD3 immunostained colon biopsy WSIs, were developedthrough active learning using the pipeline. On a hold-out test set of 36 HE and21 CD3-stained WSIs a mean intersection over union score of 96.6% and 95.3% wasachieved on epithelium segmentation. We demonstrate pathologist-levelsegmentation accuracy and clinical acceptable runtime performance and show thatpathologists without programming experience can create near state-of-the-artsegmentation solutions for histopathological WSIs using only free-to-usesoftware. The study further demonstrates the strength of open-source solutionsin its ability to create generalizable, open pipelines, of which trained modelsand predictions can seamlessly be exported in open formats and thereby used inexternal solutions. All scripts, trained models, a video tutorial, and the fulldataset of 251 WSIs with ~31k epithelium annotations are made openly availableat https://github.com/andreped/NoCodeSeg to accelerate research in the field.

Code-free development and deployment of deep segmentation models for digital pathology