  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. 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.
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. 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.
