Classification-of-brain-tumor-using-Spatiotemporal-models

Overview:

Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models (Tran et al.) can be employed as "spatiospatial" models for this task. These models have the capabilities of learning specific spatial and temporal relationship, while reducing computational costs. This is an implementation of two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolutions, and ResNet 3D, to classify high-grade glioma, low-grade glioma, and healthy brain MR Images.

(a) ResNet(2+1)D (b) ResNet Mixed Convolutions (c) ResNet 3D

Getting started:

Execute run.py, you can change the hyperparameters and settings for your experiments by overriding them in configs/config.yaml file or overriding through command line example: python run.py training.batch_size=2 for multirun use -m flag, python run.py -m training.batch_size=2,5,10.

Dataset:

The original implementation of this work was done using BraTS 2019 dataset (T1 contrast enhanced images) with high-grade and low-grade glioma samples, and using IXI (T1 images), the healthy brain images. The IXI samples were skull-stripped and resampled to be used as non-pathological images for the classification task.

Results:

Heatmaps showing the class-wise performance of the classifiers, compared using Precision, Recall, Specificity, and F1-score: (a) LGG, (b) HGG, and (c) Healthy

Confusion matrix for Pretrained ResNet Mixed Convolution (winning model)

Preprint:

Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger, and Oliver Speck, Classification of Brain Tumours in MR Images using Deep Spatiospatial Models

BibTeX:

@article{chatterjee2021classification,
  title={Classification of Brain Tumours in MR Images using Deep Spatiospatial Models},
  author={Chatterjee, Soumick and Nizamani, Faraz Ahmed and N{\"u}rnberger, Andreas and Speck, Oliver},
  journal={arXiv preprint arXiv:2105.14071},
  year={2021}
}

Special thanks to:

This work was in part conducted within the context of the International Graduate School MEMoRIAL at Otto von Guericke University (OVGU) Magdeburg, Germany, kindly supported by the European Structural and Investment Funds (ESF) under the programme "Sachsen-Anhalt WISSENSCHAFT Internationalisierung" (project no. ZS/2016/08/80646).

farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models