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MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemen...

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Autores principales: Müller, Dominik, Kramer, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814713/
https://www.ncbi.nlm.nih.gov/pubmed/33461500
http://dx.doi.org/10.1186/s12880-020-00543-7
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author Müller, Dominik
Kramer, Frank
author_facet Müller, Dominik
Kramer, Frank
author_sort Müller, Dominik
collection PubMed
description BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. 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. IMPLEMENTATION: 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. RESULTS: 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. CONCLUSIONS: 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: https://github.com/frankkramer-lab/MIScnn.
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spelling pubmed-78147132021-01-21 MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning Müller, Dominik Kramer, Frank BMC Med Imaging Software BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. 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. IMPLEMENTATION: 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. RESULTS: 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. CONCLUSIONS: 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: https://github.com/frankkramer-lab/MIScnn. BioMed Central 2021-01-18 /pmc/articles/PMC7814713/ /pubmed/33461500 http://dx.doi.org/10.1186/s12880-020-00543-7 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Müller, Dominik
Kramer, Frank
MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title_full MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title_fullStr MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title_full_unstemmed MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title_short MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
title_sort miscnn: a framework for medical image segmentation with convolutional neural networks and deep learning
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814713/
https://www.ncbi.nlm.nih.gov/pubmed/33461500
http://dx.doi.org/10.1186/s12880-020-00543-7
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