A novel deep learning graph attention network for Alzheimer’s disease image segmentation
Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000121 |
| _version_ | 1827223330266021888 |
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| author | Md Easin Hasan Amy Wagler |
| author_facet | Md Easin Hasan Amy Wagler |
| author_sort | Md Easin Hasan |
| collection | DOAJ |
| description | Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images. |
| first_indexed | 2024-03-08T00:23:24Z |
| format | Article |
| id | doaj.art-ea672b2b5230440d96eb3b8272a0e8ab |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2025-03-21T16:52:40Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-ea672b2b5230440d96eb3b8272a0e8ab2024-06-15T06:14:53ZengElsevierHealthcare Analytics2772-44252024-06-015100310A novel deep learning graph attention network for Alzheimer’s disease image segmentationMd Easin Hasan0Amy Wagler1Department of Mathematical Sciences, The University of Texas at El Paso, 500 W. University Ave., El Paso, TX, 79968, USA; Corresponding author.Department of Public Health Sciences, The University of Texas at El Paso, 500 W. University Ave., El Paso, TX, 79968, USANeuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images.http://www.sciencedirect.com/science/article/pii/S2772442524000121Image segmentationDeep learningConvolutional neural networksNeuronal cellsGraph attention networksU-Net |
| spellingShingle | Md Easin Hasan Amy Wagler A novel deep learning graph attention network for Alzheimer’s disease image segmentation Healthcare Analytics Image segmentation Deep learning Convolutional neural networks Neuronal cells Graph attention networks U-Net |
| title | A novel deep learning graph attention network for Alzheimer’s disease image segmentation |
| title_full | A novel deep learning graph attention network for Alzheimer’s disease image segmentation |
| title_fullStr | A novel deep learning graph attention network for Alzheimer’s disease image segmentation |
| title_full_unstemmed | A novel deep learning graph attention network for Alzheimer’s disease image segmentation |
| title_short | A novel deep learning graph attention network for Alzheimer’s disease image segmentation |
| title_sort | novel deep learning graph attention network for alzheimer s disease image segmentation |
| topic | Image segmentation Deep learning Convolutional neural networks Neuronal cells Graph attention networks U-Net |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000121 |
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