Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model

作者全名："Tan, Duo; Liu, Jiayang; Chen, Shanxiong; Yao, Rui; Li, Yongmei; Zhu, Shiyu; Li, Linfeng"

作者地址："[Tan, Duo; Chen, Shanxiong; Yao, Rui; Zhu, Shiyu; Li, Linfeng] South West Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China; [Liu, Jiayang; Li, Yongmei] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China"

通信作者："Chen, SX (通讯作者)，South West Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China."

来源：IEEE TRANSACTIONS ON NANOBIOSCIENCE

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001082250700012

JCR分区：Q1

影响因子：3.7

年份：2023

卷号：22

期号：4

开始页：789

结束页：799

文献类型：Article

关键词：Attention mechanism; computer-aided diagnosis technology; collateral circulation evaluation

摘要："Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients. This imaging information is of great significance in assisting doctors to determine the patient's treatment plan and prognosis. Currently, great progress has been made in the field of computer-aided diagnosis technology in medicine by using artificial intelligence. However, in related research based on deep learning algorithms, researchers usually only use single-phase data for training, lacking the temporal dimension information of multiphase image data. This makes it difficult for the model to learn more comprehensive and effective collateral circulation feature representation, thereby limiting its performance. Therefore, combining data for training is expected to improve the accuracy and reliability of collateral circulation evaluation. In this study, we propose an effective hybrid mechanism to assist the feature encoding network in evaluating the degree of collateral circulation in the brain. By using a hybrid attention mechanism, additional guidance and regularization are provided to enhance the collateral circulation feature representation across multiple stages. Time dimension information is added to the input, and multiple feature-level fusion modules are designed in the multi-branch network. The first fusion module in the single-stage feature extraction network completes the fusion of deep and shallow vessel features in the single-branch network, followed by the multi-stage network feature fusion module, which achieves feature fusion for four stages. Tested on a dataset of multi-phase cranial CTA images, the accuracy rate exceeding 90.43%. The experimental results demonstrate that the addition of these modules can fully explore collateral vessel features, improve feature expression capabilities, and optimize the performance of deep learning network model."

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