Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. (June 2021)

• Record Type: Journal Article
• Authors: Abramova, Valeriia; Clèrigues, Albert; Quiles, Ana; Figueredo, Deysi Garcia; Silva, Yolanda; Pedraza, Salvador; Oliver, Arnau; Lladó, Xavier
• Is Part Of: Computerized medical imaging and graphics. Volume 90(2021)
• Publication Date: 2021-06
• Subjects: Hemorrhagic stroke -- Segmentation -- Deep learning -- Artificial intelligence; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101908
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: We propose a U-Net-based architecture for hemorrhagic stroke segmentation in CT images. The addition of squeeze-and-excitation blocks helps improving the results. The use of brain symmetry helps to avoid segmentation of intraventricular hemorrhage. Each CT volume is analyzed in approximately 17 seconds with mean Dice accuracy of 86.2%. Abstract: Hemorrhagic stroke is the condition involving the rupture of a vessel inside the brain and is characterized by high mortality rates. Even if the patient survives, stroke can cause temporary or permanent disability depending on how long blood flow has been interrupted. Therefore, it is crucial to act fast to prevent irreversible damage. In this work, a deep learning-based approach to automatically segment hemorrhagic stroke lesions in CT scans is proposed. Our approach is based on a 3D U-Net architecture which incorporates the recently proposed squeeze-and-excitation blocks. Moreover, a restrictive patch sampling is proposed to alleviate the class imbalance problem and also to deal with the issue of intra-ventricular hemorrhage, which has not been considered as a stroke lesion in our study. Moreover, we also analyzed the effect of patch size, the use of different modalities, data augmentation and the incorporation of different loss functions on the segmentation results. All analyses have been performed using a five fold cross-validation strategy on a clinical dataset composed of 76 cases. Obtained results demonstrate that the introduction of squeeze-and-excitation blocks, together with the restrictive patch sampling and symmetric modality augmentation, significantly improved the obtained results, achieving a mean DSC of 0.86 ± 0.074, showing promising automated segmentation results.