ABSTRACT
We propose a fast and robust multi-class deep learning framework for segmenting COVID-19 lesions: Ground-Glass opacities and High opacities (including consolidations and pleural effusion), from non-contrast CT scans using convolutional Long Short-Term Memory network for self-attention. Our method allows rapid quantification of pneumonia burden from CT with performance equivalent to expert readers. The mean dice score across 5 folds was 0.8776 with a standard deviation of 0.0095. A low standard deviation between results from each fold indicate the models were trained equally good regardless of the training fold. The cumulative per-patient mean dice score (0.8775±0.075) for N=167 patients, after concatenation, is consistent with the results from each of the 5 folds. We obtained excellent Pearson correlation (expert vs. automatic) of 0.9396 (p<0.0001) and 0.9843 (p<0.0001) between ground-glass opacity and high opacity volumes, respectively. Our model outperforms Unet2d (p<0.05) and Unet3d (p<0.05) in segmenting high opacities, has comparable performance with Unet2d in segmenting ground-glass opacities, and significantly outperforms Unet3d (p<0.0001) in segmenting ground-glass opacities. Our model performs faster on CPU and GPU when compared to Unet2d and Unet3d. For same number of input slices, our model consumed 0.83x and 0.26x the memory consumed by Unet2d and Unet3d. © 2022 SPIE