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

arXiv: https://arxiv.org/abs/1910.09308 · HTML: https://mlsanity.com/paper/1910.09308/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 18 of 18)

- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy](https://mlsanity.com/paper/2105.07467/) (2021)
- [Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation](https://mlsanity.com/paper/2111.00528/) (2021)
- [MISeval: a Metric Library for Medical Image Segmentation Evaluation](https://mlsanity.com/paper/2201.09395/) (2022)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation](https://mlsanity.com/paper/2111.00533/) (2021)
- [Automated femur segmentation from computed tomography images using a deep neural network](https://mlsanity.com/paper/2101.11742/) (2021)
- [SE3D: A Framework For Saliency Method Evaluation In 3D Imaging](https://mlsanity.com/paper/2405.14584/) (2024)
- [Assessing the Role of Random Forests in Medical Image Segmentation](https://mlsanity.com/paper/2103.16492/) (2021)
- [Focal Attention Networks: optimising attention for biomedical image segmentation](https://mlsanity.com/paper/2111.00534/) (2021)
- [Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework](https://mlsanity.com/paper/2206.08182/) (2022)
- [Large Batch and Patch Size Training for Medical Image Segmentation](https://mlsanity.com/paper/2210.13364/) (2022)
- [The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data](https://mlsanity.com/paper/2306.10484/) (2023)
- [INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses](https://mlsanity.com/paper/2309.02147/) (2023)
- [Automatic Segmentation of the Kidneys and Cystic Renal Lesions on Non-Contrast CT Using a Convolutional Neural Network](https://mlsanity.com/paper/2405.08282/) (2024)
- [Medical Image Analysis for Detection, Treatment and Planning of Disease using Artificial Intelligence Approaches](https://mlsanity.com/paper/2405.11295/) (2024)
- [GRAD-Former: Gated Robust Attention-based Differential Transformer for Change Detection](https://mlsanity.com/paper/2603.01161/) (2026)

## Abstract (arXiv)

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.