Research on Network Model of Dentate Gyrus Based on Bionics

Zhang, Jin; Wu, Xiaoping; Zhang, Shiwen; Niu, Xiamu; Li, Guang; Tian, Tiantian

doi:10.1155/2021/4609741

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The hippocampus is a portion of the brain located between the cerebral thalamus and the medial temporal lobe; it is a crucial organ that is responsible for storing and organizing memory [1]. Research on the main function and basic structure of the hippocampus is essential for understanding the working principles of the brain and the pathogenesis of neurodegenerative diseases and developing treatment methods.…”

Section: Introductionmentioning

confidence: 99%

Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network

Jiang

Liang

et al. 2023

Applied Sciences

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Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net’s encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer’s Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms.

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Section: Introductionmentioning

confidence: 99%

Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network

Jiang

Liang

et al. 2023

Applied Sciences

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“…Guimei Cao et al [23] introduced contextual information into SSD to detect a small object. In addition, there are also some olfactory neural networks [24,25], etc. for detection.…”

Section: Introductionmentioning

confidence: 99%

MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

Sun

Zhang²,

Qu³

et al. 2022

Applied Sciences

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It is an essential measure for workers to wear safety helmets when entering the construction site to prevent head injuries caused by object collision and falling. This paper proposes a lightweight algorithm for helmet-wearing detection based on YOLOV5, which is faster and more robust for helmet detection in natural construction scenarios. In this paper, the MCA attention mechanism is embedded in the backbone network to help the network extract more productive information, reduce the missed detection rate of small helmet objects and improve detection accuracy. In order to ensure the safety of workers in construction, it is necessary to detect whether the construction workers are wearing safety helmets in real-time to achieve monitoring on-site. A channel pruning strategy is proposed on the MCA-YOLOv5 algorithm to compress it, realizing the optimal large-scale model into ultrasmall models for real-time detection on embedded or mobile devices. The experimental results on the public data set show that the model parameter volume is reduced by 87.2%, and the detection speed is increased by 53.5%, even though the MCA-YOLOv5-light reduces the mAP slightly.

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Microstructure of the dentate gyrus and spontaneous alternation behaviour of male Wistar rats following Rauvolfia vomitoria and Gongronema latifolium extracts administration

Ekong,

Essien,

Bassey

et al. 2024

J. Toxicol. Stud.

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Rauvolfia vomitoria (RV) and Gongronema latifolium (GL) are medicinal plants used for the local treatment of various health issues. Their activities on the brain motivated this investigation on the histology and immunohistochemistry of the dentate gyrus and spontaneous alternation behaviour (SAB) of adult Wistar rats following RV root bark and GL leaf extract administrations. Twenty young adult Wistar rats (130–160 g) were assigned into four groups: Group 1 served as the control (5 mL/kg of distilled water placebo), while the test groups 2–4 were, respectively, singly administered 200 of mg/kg of RV, 200 of mg/kg of GL, and their combination. The administrations were oral and lasted for seven days. A T-maze SAB test was carried out, and the animals were sacrificed immediately after ketamine hydrochloride intraperitoneal anaesthesia. Serial sections of the hippocampal region from perfused rat brains were stained with Cresyl fast violet and immunolabelled with neuronal nuclei (NeuN) for neurons and glial fibrillary acidic protein (GFAP) for astrocytes. Results indicated that SAB was significantly (p < 0.05) lower in the test groups. Histologically, Nissl was less distributed in the RV and GL-only groups but not in the combined group, while there was less NeuN positivity in the RV group, with the GL and RV + GL groups not affected. There was less positive GFAP expression in individual RV and GL groups, but not in the RV + GL combined group, all compared with the control. In conclusion, the combination of RV and GL did not improve SAB but modulated Nissl, NeuN, and GFAP expression in the dentate gyrus.

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Research on Network Model of Dentate Gyrus Based on Bionics

Cited by 3 publications

References 19 publications

Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network

Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network

MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

Microstructure of the dentate gyrus and spontaneous alternation behaviour of male Wistar rats following Rauvolfia vomitoria and Gongronema latifolium extracts administration

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