Diffusion-weighted magnetic resonance imaging (DWI) is sensitive to acute ischemic stroke and is a common diagnostic method for the stroke. However, the diagnostic result relies on the visual observation of neurologists which may vary from doctor to doctor under different circumstance. And manual segmentation is often a time-consuming and subjective process. The time from onset to thrombus removal has a significant impact on the prognosis of patients with acute ischemic stroke. The shorter the time, the better the prognosis. For this purpose we present a novel framework to quickly and automatically segment the ischemic stroke lesions on DWI. We mainly have three contributions: firstly, we design a detection and segmentation network (DSN) to solve the two kinds of data imbalance; secondly, we propose a triple-branch DSN architecture, used for extracting different plane feature respectively; thirdly, we propose a multi-plane fusion network (MPFN), which aims to make final prediction more accurate. Extensive experiments on ISLES2015 SSIS DWI sequence dataset demonstrate the superiority of our proposed segmentation method. The dice reached 62.2% and the sensitivity reached 71.7%.

Ischemic stroke is the most common stroke and the leading cause of disability and death in the world. Computed tomography (CT) is a popular and economical diagnostic device for the stroke, However the ischemic stroke lesions are not evident on CT images and the diagnostic result relies on the visual observation of neurologists, which may vary from doctor to doctor. To facilitate the treatment, a computer-aided detection algorithm on CT images is proposed to help clinician for the ischemic stroke screening. In order to obtain accurate lesion annotation on CT images, novel automatic algorithms are developed to achieve image pairing, calibration, and registration. Then, a new framework with the symmetric feature extraction and comparison is proposed to identify and locate the ischemic stroke lesion. Experimental results show that this method achieves 75% of DICE in the detection of ischemic stroke lesions, which is higher than other methods by 4%. Its competitive results compared with seven latest methods is shown in terms of extensive qualitative and quantitative evaluation. This method can accurately detect the lesion in the CT images through the comparison of symmetric regional features, which has contributed to the clinical diagnosis of ischemic stroke.