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Automated Segmentation of Salvageable Ischemic Brain Tissue using Convolutional Neural Networks with DWI and FLAIR MRI

Ryan Andrew Rava^1,2, Kenneth V. Snyder^2,3, Muhammad Waqas^2,3, Elad I. Levy^2,3, Jason M. Davies^2,3, Adnan H. Siddiqui^2,3, and Ciprian N. Ionita^1,2,3
¹Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, ²Canon Stroke and Vascular Research Center, Buffalo, NY, United States, ³Neurosurgery, University at Buffalo, Buffalo, NY, United States

Two neural networks were developed to segment penumbra using FLAIR and DWI. Metrics comparing predictions with ground truth penumbra ((dual network, multi-input network): Dice=(0.61, 0.61), PPV=(0.59, 0.64)) indicate a multi-input network is more capable of segmenting penumbra.

Figure 3: Top images indicate DWI and FLAIR slices fed into each network to segment the penumbra seen in the bottom images. The dual network prediction indicates the subtraction of FLAIR segmented infarct from DWI segmented ischemic tissue. The multi-input prediction indicates predicted penumbra using both DWI and FLAIR as network input and the red line is an outline of the ground truth penumbra for this slice. Dual and multi-input Dice coefficients for this slice are 0.83 and 0.78 respectively.

Table 1. Dice Coefficients, sensitivity, positive predictive values, infarct/ischemic tissue/penumbra volume differences, and Spearman correlation coefficients, with 95% confidence intervals, for each network’s segmentation performance of either ischemic tissue (DWI), infarct (FLAIR), or penumbra (Dual Network and Multi-Input).