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 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/frank kramer-lab/MIScnn.

Automated summary

MIScnn is an open-source Python library designed to provide an intuitive API for fast construction of medical image segmentation pipelines, offering high configurability and multiple open interfaces for full pipeline customization. By running cross-validation on the Kidney Tumor Segmentation Challenge 2019 data set, MIScnn demonstrated powerful predictive capabilities based on the standard 3D U-Net model, showing that researchers can quickly set up a complete medical image segmentation pipeline with just a few lines of code.