1. Automating stroke lesion segmentation in brain images using a multi-model multi-path convolutional neural network
Yunzhe

2. Why automatic lesion identification?
Reduce time to identify lesion
Manual lesion identification can take 4 to 10 hours whereas automatic takes minutes (Wilke et. al. NeuroImage 2011)
Human inter-rater variability
Independent raters agreement ranges from 67% to 73%
Fiez et. al. Human Brain Mapping 2000
Sook-Lei et. al. Scientific Data 2018

3. What does our system do?
Our system takes in three views of the MRI scan (each normalized in three different ways). We can see the human identified lesion in each view.
The bottom images contain the prediction given by our system overlaid on the same input images.

4. Our system

5. Our system

6. First phase

7. First phase’s results

8. 3D CNN Post-processing

9. Before 3D CNN Post Processing After 3D CNN Post Processing

10. Experimental performance study
MRI images from three different sources
Kessler Foundation (N = 25)
Medical College of Wisconsin (N = 20)
ATLAS public dataset (N = 54)
All datasets contain human curated lesions that serve as ground truth in our experiments and in training our models
Do cross-study and cross-validation
Measure accuracy with the commonly used Dice coefficient

11. 5-fold cross-validation on all data

12. Big vs small lesions
Large lesions are generally easier to identify than smaller ones
Define small lesions as below 10,000 pixels in size and large otherwise
We perform better than previous methods on both big and small lesions:
Statistically better than the popular DeepMedic on small lesions
Statistically better than UNet on large lesions

13. Big vs small lesions
Raincloud plots of accuracies of our system

14. Thank you