Source: https://arxiv.org/pdf/1810.11654.pdf
Keras implementation of the paper 3D MRI brain tumor segmentation using autoencoder regularization by Myronenko A. (https://arxiv.org/abs/1810.11654). The author (team name: NVDLMED) ranked #1 on the BraTS 2018 leaderboard using the model described in the paper.
This repository contains the model complete with the loss function, all implemented end-to-end in Keras. The usage is described in the next section.
-
Download the file
model.py
and keep in the same folder as your project notebook/script. -
In your python script, import
build_model
function frommodel.py
.from model import build_model
It will download an additional script needed for the implementation,
group_norm.py
, which contains keras implementation for the group normalization layer. -
Note that the input MRI scans you are going to feed need to have 4 dimensions, with channels-first format. i.e., the shape should look like (c, H, W, D), where:
c
, the no.of channels are divisible by 4.H
,W
,D
, which are height, width and depth, respectively, are all divisible by 24, i.e., 16. This is to get correct output shape according to the model.
-
Now to create the model, simply run:
model = build_model(input_shape)
where,
input_shape
is a 4-tuple (channels, Height, Width, Depth).
If you encounter any issue or have a feedback, don't hesitate to raise an issue.