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

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

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.

Our system

Our system

First phase

First phase’s results

3D CNN Post-processing

Before 3D CNN Post Processing After 3D CNN Post Processing

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

5-fold cross-validation on all data

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

Big vs small lesions Raincloud plots of accuracies of our system

Thank you