1 research outputs found
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP)
images is important for accurate diagnosis of stroke in acute care units.
However, it is challenged by low image contrast and resolution of the perfusion
parameter maps, in addition to the complex appearance of the lesion. To deal
with this problem, we propose a novel framework based on synthesized pseudo
Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better
image quality for more accurate segmentation. Our framework consists of three
components based on Convolutional Neural Networks (CNNs) and is trained
end-to-end. First, a feature extractor is used to obtain both a low-level and
high-level compact representation of the raw spatiotemporal Computed Tomography
Angiography (CTA) images. Second, a pseudo DWI generator takes as input the
concatenation of CTP perfusion parameter maps and our extracted features to
obtain the synthesized pseudo DWI. To achieve better synthesis quality, we
propose a hybrid loss function that pays more attention to lesion regions and
encourages high-level contextual consistency. Finally, we segment the lesion
region from the synthesized pseudo DWI, where the segmentation network is based
on switchable normalization and channel calibration for better performance.
Experimental results showed that our framework achieved the top performance on
ISLES 2018 challenge and: 1) our method using synthesized pseudo DWI
outperformed methods segmenting the lesion from perfusion parameter maps
directly; 2) the feature extractor exploiting additional spatiotemporal CTA
images led to better synthesized pseudo DWI quality and higher segmentation
accuracy; and 3) the proposed loss functions and network structure improved the
pseudo DWI synthesis and lesion segmentation performance.Comment: 14 pages, 10 figure