Automatic Image Segmentation in Nasopharyngeal Carcinoma
Summary
Automatic image segmentation in nasopharyngeal carcinoma has become indispensable for precise delineation of tumour volumes in radiotherapy planning. Traditional manual contouring is labour-intensive and subject to inter-observer variability, motivating the rise of deep learning approaches that deliver consistent, reproducible outputs. Key challenges include wide variation in tumour morphology, low contrast at tumour boundaries and severe imbalance between tumour and surrounding tissues. Recent models address these issues by combining encoder–decoder architectures with attention mechanisms, multiscale feature extraction and integration of clinical staging information. Advances in three-dimensional frameworks have enabled volumetric segmentation on computed tomography, while lightweight two-dimensional networks facilitate rapid inference in resource-constrained settings. The global significance of these technologies lies in improved treatment accuracy, reduced planning time and enhanced accessibility of advanced oncology services.
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Automatic Image Segmentation in Nasopharyngeal Carcinoma publication trend
The graph below shows the total number of articles in automatic image segmentation in nasopharyngeal carcinoma across all publications each year (not limited to Nature Index journals).
Technical terms
Convolutional neural network (CNN): A deep learning framework employing convolving filters to automatically extract hierarchical spatial features from image inputs.
U-Net: A CNN architecture with encoder–decoder structure and skip connections that captures context and preserves high-resolution details for precise segmentation.
Dice similarity coefficient (DSC): A statistical metric measuring the overlap between predicted and reference segmentation masks; values range from 0 (no overlap) to 1 (perfect agreement).
Attention mechanism: A strategy enabling neural networks to focus selectively on the most informative regions of feature maps, improving segmentation of complex structures.
Multiscale feature extraction: The technique of analysing image information at multiple spatial resolutions to detect anatomical structures of varying sizes.
References
- Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study. Frontiers in Oncology (2022).
- AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution. Frontiers in Oncology (2022).
- Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images. Sensors (2022).
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