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Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn.<\/jats:p><\/jats:sec><jats:sec><jats:title>Implementation<\/jats:title><jats:p>The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I\/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository:<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/frankkramer-lab\/MIScnn\">https:\/\/github.com\/frankkramer-lab\/MIScnn<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12880-020-00543-7","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T19:02:51Z","timestamp":1610996571000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-9885","authenticated-orcid":false,"given":"Dominik","family":"M\u00fcller","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2857-7122","authenticated-orcid":false,"given":"Frank","family":"Kramer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"543_CR1","first-page":"54","volume":"29","author":"P Aggarwal","year":"2011","unstructured":"Aggarwal P, Vig R, Bhadoria S, Dethe CG. 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