Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm
Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, a...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/4494411 |
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| author | Hanqing Liu Xiaojun Li Jin Wei Xiaodong Kang |
| author_facet | Hanqing Liu Xiaojun Li Jin Wei Xiaodong Kang |
| author_sort | Hanqing Liu |
| collection | DOAJ |
| description | Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms. |
| format | Article |
| id | doaj-art-a6b7f9f47c6b4089816c07daf799b633 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-a6b7f9f47c6b4089816c07daf799b6332025-02-03T01:22:58ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/4494411Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection AlgorithmHanqing Liu0Xiaojun Li1Jin Wei2Xiaodong Kang3School of Medical TechnologySchool of Medical TechnologyTianjin Third Central HospitalSchool of Medical TechnologyStroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms.http://dx.doi.org/10.1155/2022/4494411 |
| spellingShingle | Hanqing Liu Xiaojun Li Jin Wei Xiaodong Kang Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm Discrete Dynamics in Nature and Society |
| title | Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm |
| title_full | Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm |
| title_fullStr | Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm |
| title_full_unstemmed | Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm |
| title_short | Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm |
| title_sort | cerebral arterial stenosis detection based on a retained two stage detection algorithm |
| url | http://dx.doi.org/10.1155/2022/4494411 |
| work_keys_str_mv | AT hanqingliu cerebralarterialstenosisdetectionbasedonaretainedtwostagedetectionalgorithm AT xiaojunli cerebralarterialstenosisdetectionbasedonaretainedtwostagedetectionalgorithm AT jinwei cerebralarterialstenosisdetectionbasedonaretainedtwostagedetectionalgorithm AT xiaodongkang cerebralarterialstenosisdetectionbasedonaretainedtwostagedetectionalgorithm |