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, images) or new user-provided data. The launch pad also provides access to a detailed user manual, and is the recommended starting point for a new user

, DPNUnet (Engine) Plugin installation link

, This method uses a Dual, This plugin implements a deep learning model for segmenting nuclei in microscopy images and is based on the winning entry of the 2018 Kaggle Data Science Bowl on nuclear segmentation

, In practice, multiple plugins can be selected from a central launch pad: the annotation plugin ("annotate images") permits to browse and annotate training images by manually outlining nuclei; the mask generation plugin ("generate masks") can then be used to create masks based on these annotations; the training plugin ("train with data from the engine") then uses these masks as targets to re-train the DPNUnet model; finally, the trained model can be used for segmenting user

, Code for segmentation