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Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection

Release v1.0

It contains the code described in the paper "Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection".

1 - Abstract

Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. A loss function based on the focal Tversky index addresses the data imbalance issue. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved.

alt text

2 - Code

Code for this repository will be uploaded upon paper acceptance

2.1 - Table Validation Results

Model Input Layer
Dice coeff. (Avg)±SD
PMs MIP NIHSS Penumbra Core Penumbra Core
Model_F(PMs) X Frozen 0.71±0.1 0.37±0.3 0.27±0.3 0.22±0.3
Model_F(PMs,M) X X 0.69±0.2 0.36±0.3 0.29±0.3 0.20±0.3
Model_F(PMs,N) X X 0.70±0.2 0.36±0.3 0.29±0.3 0.16±0.2
Model_F(PMs,M,N) X X X 0.68±0.2 0.34±0.3 0.30±0.3 0.18±0.3
Model_U(PMU) X Unfrozen 0.70±0.2 0.34±0.3 0.29±0.4 0.24±0.3
Model_F(PMs,M) X X 0.70±0.2 0.36±0.3 0.34±0.3 0.24±0.3
Model_U(PMs,N) X X 0.72±0.2 0.36±0.3 0.29±0.3 0.23±0.3
Model_U(PMs,M,N) X X X 0.71±0.2 0.36±0.3 0.32±0.3 0.22±0.3
Model_G(PMs) X Gradual
0.71±0.2 0.35±0.3 0.30±0.3 0.19±0.3
Model_G(PMs,M) X X 0.69±0.2 0.35±0.3 0.34±0.6 0.22±0.4
Model_G(PMs,N) X X 0.72±0.2 0.37±0.3 0.31±0.3 0.21±0.3
Model_G(PMs,M,N) X X X 0.68±0.2 0.34±0.3 0.31±0.3 0.18±0.3

2.2 - Link to paper

3 - Dependecies:

pip install -r requirements.txt

4 - Usage

Assuming that you already have a dataset to work with, you can use a json file to define the setting of your model.

Refer to Setting_explained.json for explanations of the various settings.

4.1 Train/Test

Usage: python gpu sname
                [-h] [-v] [-d] [-o] [-s SETTING_FILENAME] [-t TILE] [-dim DIMENSION] [-c {2,3,4}]

    positional arguments:
      gpu                   Give the id of gpu (or a list of the gpus) to use
      sname                 Select the setting filename

    optional arguments:
      -h, --help            show this help message and exit
      -v, --verbose         Increase output verbosity
      -d, --debug           DEBUG mode
      -o, --original        Set the shape of the testing dataset to be compatible with the original shape
                            (T,M,N) [time in front]
      -pm, --pm             Set the flag to train the parametric maps as input
      -t TILE, --tile TILE  Set the tile pixels dimension (MxM) (default = 16)
      -dim DIMENSION, --dimension DIMENSION
                            Set the dimension of the input images (width X height) (default = 512)
      -c {2,3,4}, --classes {2,3,4}
                            Set the # of classes involved (default = 4)
      -w, --weights         Set the weights for the categorical losses

5 - How to cite our work

The code is released free of charge as open-source software under the GPL-3.0 License. Please cite our paper when you have used it in your study.

  title={Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection},
  author={Tomasetti, Luca and Khanmohammadi, Mahdieh and Engan, Kjersti and H{\o}llesli, Liv Jorunn and Kurz, Kathinka D{\ae}hli},
  booktitle={Proceedings of the Northern Lights Deep Learning Workshop},

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