3D dissimilar-siamese-u-net for hyperdense Middle cerebral artery sign segmentation. (June 2021)

• Record Type: Journal Article
• Authors: You, Jia; Yu, Philip L.H.; Tsang, Anderson C.O.; Tsui, Eva L.H.; Woo, Pauline P.S.; Lui, Carrie S.M.; Leung, Gilberto K.K.; Mahboobani, Neeraj; Chu, Chi-yeung; Chong, Wing-ho; Poon, Wai-lun
• Is Part Of: Computerized medical imaging and graphics. Volume 90(2021)
• Publication Date: 2021-06
• Subjects: Acute ischemic stroke -- Hyperdense Middle cerebral artery sign -- Segmentation -- Deep learning; 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.2021.101898
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Fully automated pre-processing steps allows geometrical alignment of all head CT images. Siamese module enables the input of left and right hemisphere simultaneously and separately. Dissimilar block in the DSU-Net effectively probe the feature representation of dissimilarity between bilateral hemispheres. Results obtained by our approach outperformed the state-of-the-art methods. Data were from multiple hospitals under various protocols, indicating the possibility of broad clinical applicability. Abstract: The hyperdense middle cerebral artery sign (HMCAS) representing a thromboembolus has been declared as a vital CT finding for intravascular thrombus in the diagnosis of acute ischemia stroke. Early recognition of HMCAS can assist in patient triage and subsequent thrombolysis or thrombectomy treatment. A total of 624 annotated head non-contrast-enhanced CT (NCCT) image scans were retrospectively collected from multiple public hospitals in Hong Kong. In this study, we present a deep Dissimilar-Siamese-U-Net (DSU-Net) that is able to precisely segment the lesions by integrating Siamese and U-Net architectures. The proposed framework consists of twin sub-networks that allow inputs of left and right hemispheres in head NCCT images separately. The proposed Dissimilar block fully explores the feature representation of the differences between the bilateral hemispheres. Ablation studies were carried out to validate the performance of various components of the proposed DSU-Net. Our findings reveal that the proposed DSU-Net provides a novel approach for HMCAS automatic segmentation and it outperforms the baseline U-Net and many state-of-the-art models for clinical practice.