Introduction: Predicting the final infarct for acute stroke from acute imaging is valuable for triage and prognosis (1). We developed and tested a Deep Learning model, based on a 3D Convolutional Neural Network (CNN) architecture, to predict the lesion based on a 30-90 day scan from acute images.
Method: The model has 3 advantages compared with traditional CNN (Figure):
1. 3D CNN more efficiently utilizes spatial information across multiple slices.
2. Patches instead of entire images are used as inputs. With a patch-based approach, we avoid the impact of minimally-relevant information from distant voxels. Training on patches also prevents over-fitting by augmenting each image into thousands of samples.
3. Multi-scale structure is used by processing patches with different resolutions. Segmentation based on a single scale image cannot fully capture varying local information and may miss contextual information. We use two scales of patches to learn both local and global context.
The model was trained and tested using the MICCAI ISLES 2017 challenge dataset (2), which consists of 43 cases with acute ADC and PWI maps paired with annotated final infarct segmentations at day 30-90. Dice score coefficient (DSC) and AUC for segmentation were used as quality metrics.
Results: Using 70% of dataset for training and rest for testing, we achieved a DSC of 0.43±0.18 and 0.90 for AUC. As a comparison, the winning entry for the ISLES 2016 challenge achieved a DSC of 0.31, while several previous research works (threshold based, cluster based, Generalized Linear Model, etc.) achieve up to 0.84 AUC (1).
Conclusion: We demonstrate that a deep learning approach can predict the final stroke lesion based on acute diffusion and perfusion neuroimaging data. Given its inherent speed, high performance, and capacity for further training, deep learning is a promising method for stroke lesion outcome prediction.
1. Rekik et al., Neuroimage Clin 2012;