  Ischemic stroke is a common disease in the elderly population, which can
cause long-term disability and even death. However, the time window for
treatment of ischemic stroke in its acute stage is very short. To fast localize
and quantitively evaluate the acute ischemic stroke (AIS) lesions, many
deep-learning-based lesion segmentation methods have been proposed in the
literature, where a deep convolutional neural network (CNN) was trained on
hundreds of fully labeled subjects with accurate annotations of AIS lesions.
Despite that high segmentation accuracy can be achieved, the accurate labels
should be annotated by experienced clinicians, and it is therefore very
time-consuming to obtain a large number of fully labeled subjects. In this
paper, we propose a semi-supervised method to automatically segment AIS lesions
in diffusion weighted images and apparent diffusion coefficient maps. By using
a large number of weakly labeled subjects and a small number of fully labeled
subjects, our proposed method is able to accurately detect and segment the AIS
lesions. In particular, our proposed method consists of three parts: 1) a
double-path classification net (DPC-Net) trained in a weakly-supervised way is
used to detect the suspicious regions of AIS lesions; 2) a pixel-level K-Means
clustering algorithm is used to identify the hyperintensive regions on the
DWIs; and 3) a region-growing algorithm combines the outputs of the DPC-Net and
the K-Means to obtain the final precise lesion segmentation. In our experiment,
we use 460 weakly labeled subjects and 15 fully labeled subjects to train and
fine-tune the proposed method. By evaluating on a clinical dataset with 150
fully labeled subjects, our proposed method achieves a mean dice coefficient of
0.642, and a lesion-wise F1 score of 0.822.
