This framework is provided to perform pixel-level segmentation of human liver, spleen, pancreas and kidney, based on MR images provided by German National Cohort(NAKO Dataset), using deep-learning method, and visualized the results. It establishes all functionality needed to operate on 3D images with a patch-based architecture.
NAKO Dataset：
- Over 3400 labeled MRI images from thousands patients
- Over 500 MRI images for evaluation
Used network architectures including 3d u-net, non-local neural network, attention u-net are proposed.
Arxiv:
use pip3 (with a venv)
pip3 install -e .
if it fails consider
pip3 install -e . --user
For training use
nohup python3 -u train.py > file_out 2> file_err &
For prediction use
nohup python3 -u evaluate.py > file_out 2> file_err &
Inspired by the popular NLP Transformer architecture proposed by Google in 2017, an architecture of similar idea is proposed for image processing, the non-local neural networks.
It can capture the long-range dependencies between pixels more properly, check the paper from Wang Xiaolong https://arxiv.org/abs/1711.07971
Its architecture as following:
Baseline architecture is a 4-stages 3d u-net, as following:
Achieve an average accurancy of 97% of all classes.
