Attention-Guided deep atrous-residual U-Net architecture for automated gland segmentation in colon histopathology images
In digital pathology, gland segmentation plays a dominant part in the diagnosis and quantification of colon cancer. Thus, this paper presents a clinically relevant deep learning-based automated gland segmentation technique called Attention-Guided deep Atrous-Residual U-Net that aims to seize small a...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ae08adc062c449289554a5e385e86c3e |
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| Sumario: | In digital pathology, gland segmentation plays a dominant part in the diagnosis and quantification of colon cancer. Thus, this paper presents a clinically relevant deep learning-based automated gland segmentation technique called Attention-Guided deep Atrous-Residual U-Net that aims to seize small and intricate variations in medical images whilst preserving spatial information. It is a modified U-Net architecture that comprises enhanced learning capability as its Atrous-Residual units extract more discriminative and multi-level feature representations while alleviating the diminishing-gradient issue. Its Attention units extract more gland-specific detailed features, perform target refinement, and help in comparable semantic features concatenation. Its Transitional Atrous units incorporate dense multi-scale features and handle resolution degradation problems. Also, various data augmentation and stain normalization techniques are utilized to improve its generalization capability. Extensive experiments are performed using two publicly available datasets (GlaS challenge and CRAG) and a private hospital dataset HosC that helps to build invariance of the proposed architecture towards digital variability present in clinical applications and verifying its robustness. The proposed model achieves competitive results over existing state-of-art techniques having a significant improvement in F1-score (at least 2% for GlaS and 3.7% for CRAG), Object-Dice Index (at least 2.3% for GlaS and 3.5% for CRAG), and Object-Hausdorff Distance (at least 2.89% for GlaS and 3.11% for CRAG). Also, for the private HosC dataset, it achieves an F1-score of 0.947, Object-Dice Index of 0.912, and Object-Hausdorff Distance of 89.78. In addition, the final output results are validated by multiple pathologists in terms of their score i.e., for GlaS (0.9184 for test A and 0.91 for test B), 0.9032 for CRAG, and 0.904 for HosC that verify its clinical relevancy and suitability in the facilitation of the proposed model for gland detection and segmentation applications in clinical practice. It can further aid the pathologists to create a precise diagnosis and plan of treatment. |
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