MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

TL;DR

MIScnn is an open-source Python framework that simplifies the development of medical image segmentation pipelines using deep learning, enabling rapid setup and customization for various datasets and models.

Contribution

This paper introduces MIScnn, a flexible, easy-to-use library that integrates data handling, preprocessing, models, and evaluation for medical image segmentation.

Findings

Successfully applied to Kidney Tumor Segmentation Challenge data

Enabled rapid pipeline setup with minimal code

Achieved effective segmentation with standard 3D U-Net

Abstract

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.