Journal
IEEE ACCESS
Volume 6, Issue -, Pages 57006-57016Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2872939
Keywords
Deep learning; stroke lesion segmentation; residual network (ResNet); convolutional neural network (CNN); fully convolutional network (FCN)
Categories
Funding
- National Natural Science Foundation of China [61501262, 61571244, 61671254]
- Ministry of Education, Culture, Sports, Science and Technology, Japan [16K00335]
- Tianjin Research Program of Application Foundation and Advanced Technology [15JCYBJC51600, 16YFZCSF00540]
- Grants-in-Aid for Scientific Research [16K00335] Funding Source: KAKEN
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The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While a quantitative evaluation of the stroke lesions on the magnetic resonance images (MRIs) is effective in clinical diagnosis, manually segmenting the stroke lesions is commonly used, which is, however, a tedious and time-consuming task. Therefore, how to segment the stroke lesions in a fully automated manner has recently extracted extensive attentions. Considering that the clinically acquired MRIs usually have thick slices, we propose a 2D-slice-based segmentation method. In particular, we use multi-spectral MRIs, i.e., diffusion weighted image, apparent diffusion coefficient, and T2-weighted image, as input, and propose a residual-structured fully convolutional network (Res-FCN). The proposed Res-FCN is trained and evaluated on a large data set with 212 clinically acquired MRIs, which achieves a mean dice coefficient of 0.645 with a mean number of false negative lesions of 1.515 per subject. The proposed Res-FCN is further evaluated on a public data set, i.e., ISLES2015-SISS, which presents a very competitive result among all 2D-slice-based segmentation methods.
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