  CT perfusion (CTP) has been used to triage ischemic stroke patients in the
early stage, because of its speed, availability, and lack of contraindications.
Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow
(CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed
from CTP data. However, CTP data or the perfusion parameters, are ambiguous to
locate the infarct core or tissue at risk (penumbra), which is normally
confirmed by the follow-up Diffusion Weighted Imaging (DWI) or perfusion
diffusion mismatch. In this paper, we propose a novel generative modelbased
segmentation framework composed of an extractor, a generator and a segmentor
for ischemic stroke lesion segmentation. First, an extractor is used to
directly extract the representative feature images from the CTP feature images.
Second, a generator is used to generate the clinical relevant DWI images using
the output from the extractor and perfusion parameters. Finally, the segmentor
is used to precisely segment the ischemic stroke lesion using the generated DWI
from the generator. Meanwhile, a novel pixel-region loss function, generalized
dice combined with weighted cross entropy, is used to handle data unbalance
problem which is commonly encountered in medical image segmentation. All
networks are trained end-to-end from scratch using the 2018 Ischemic Stroke
Lesion Segmentation Challenge (ISLES) dataset and our method won the first
place in the 2018 ischemic stroke lesions segmentation challenge in the test
stage.
