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A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 425 |
| 2026-07-25 | 425 |
| 2026-07-28 | 425 |
| 2026-07-30 | 425 |
| 2026-07-31 | 425 |
| 2026-08-10 | 425 |
| 2026-08-11 | 425 |
| 2026-08-18 | 425 |
| 2026-08-21 | 422 |
| 2026-09-10 | 423 |
| 2026-09-20 | 423 |
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[](https://www.python.org/)
[](https://travis-ci.org/github/frankkramer-lab/MIScnn)
[](https://codecov.io/gh/frankkramer-lab/miscnn)
[](https://pypi.org/project/miscnn/)
[](https://pypistats.org/packages/miscnn)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
**MIScnn provides several core features:**
- 2D/3D medical image segmentation for binary and multi-class problems
- Data I/O, preprocessing and data augmentation for biomedical images
- Patch-wise and full image analysis
- State-of-the-art deep learning model and metric library
- Intuitive and fast model utilization (training, prediction)
- Multiple automatic evaluation techniques (e.g. cross-validation)
- Custom model, data I/O, pre-/postprocessing and metric support
- Based on Keras with Tensorflow as backend
## Resources
- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
- MIScnn on Zenodo: [Zenodo - MIScnn](https://doi.org/10.5281/zenodo.3970863)
- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)
## Getting started: 60 seconds to a MIS pipeline
```python
# Import the MIScnn module
import miscnn
# Create a Data I/O interface for kidney tumor CT scans in NIfTI format
from miscnn.data_loading.interfaces import NIFTI_interface
interface = NIFTI_interface(pattern="case_000[0-9]*", channels=1, classes=3)
# Initialize data path and create the Data I/O instance
data_path = "/home/mudomini/projects/KITS_challenge2019/kits19/data.original/"
data_io = miscnn.Data_IO(interface, data_path)
# Create a Preprocessor instance to configure how to preprocess the data into batches
pp = miscnn.Preprocessor(data_io, batch_size=4, analysis="patchwise-crop",
patch_shape=(128,128,128))
# Create a deep learning neural network model with a standard U-Net architecture
from miscnn.neural_network.architecture.unet.standard import Architecture
unet_standard = Architecture()
model = miscnn.Neural_Network(preprocessor=pp, architecture=unet_standard)
```
Congratulations to your ready-to-use Medical Image Segmentation pipeline including data I/O, preprocessing and data augmentation with default setting.
Let's run a model training on our data set. Afterwards, predict the segmentation of a sample using the fitted model.
```python
# Training the model with 80 samples for 500 epochs
sample_list = data_io.get_indiceslist()
model.train(sample_list[0:80], epochs=500)
# Predict the segmentation for 20 samples
pred = model.predict(sample_list[80:100], return_output=True)
```
Now, let's run a 5-fold Cross-Validation with our model, create automatically evaluation figures and save the results into the directory "evaluation_results".
```python
from miscnn.evaluation impExcerpt of 6,670 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f792268d177058af, topic:deep-learning, topic:neural-network, topic:tensorflow
matched fp:f792268d177058af, topic:computer-vision, desc:image segmentation, readme:image segmentation