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Abstract WMP22: Synthetic Image Based Deep Learning of Stroke DWI Lesion Segmentation


 Introduction:
 The application of deep learning to stroke image analysis (and medical images in general) faces two major challenges: first, it requires a large number of images to train, which is difficult to obtain. Second, the accurate outlining of infarcts is tedious, requires a high level of expertise, is subjective and error prone. The purpose of this work was to produce a large set of diffusion-weighted images (DWI) with perfectly defined realistic-appearing synthetic acute stroke lesions and to compare the segmentation performance of a deep neural network trained on these DWI scans with synthetic stroke lesions to a network trained on DWI scans of real stroke patients with stroke lesions manually outlined by neuroradiologists.
 
 
 Methods:
 449 DWI scans with stroke lesions (72 ± 14y) and 2027 normal DWI scans (38 ± 24y) were coregistered, resampled, cropped to 96 x 80 x 40 voxels, normalized and divided into training/testing sets (80/20%). Stroke lesions were manually segmented by 3 neuroradiologists. 2000 synthetic 3D stroke DWI were produced by fusing thresholded (min 8%) signal increase of random DWI lesions to random coregistered normal DWI (
 A
 ). A 3D U-Net (Tensorflow, depth 3, initial 64 feature maps doubled with each downsampling, bottleneck 3; hyperparameters optimized with cross-validation) was trained separately on 3 datasets: human-labeled real stroke cases (HL); 2000 synthetic cases (S); human-labeled real stroke cases + 2000 synthetic cases (HL+S).
 
 
 Results:
 The model trained on the human-labeled real stroke cases + 2000 synthetic cases (average dice coefficient between 300 and 600 epochs= 0.66±0.14) significantly outperformed the model trained on the 2000 synthetic cases only (0.60±0.14) and the model trained on human-labeled real stroke cases only (0.55±0.18; p<10
 -29
 for all comparisons) (
 B-C
 ).
 
 
 Conclusions:
 Deep learning segmentation of acute stroke lesions was significantly improved and was more stable by using synthetically generated images.
 
 
 
