Ordinal Semantic Segmentation Applied to Medical and Odontological Images
Summary: arXiv:2603.26736v1 Announce Type: cross
Abstract: Semantic segmentation consists of assigning a semantic label to each pixel according to predefined classes. This process facilitates the understanding of object appearance and spatial relationships, playing an important role in the global interpretation of image content. Although modern deep learning approaches achieve high accuracy, they often ignore ordinal relationships among classes, which may encode important domain knowledge for scene interpretation.
In this work, loss functions that incorporate ordinal relationships into deep neural networks are investigated to promote greater semantic consistency in semantic segmentation tasks. These loss functions are categorized as:
- Unimodal: These losses constrain the predicted probability distribution according to the class ordering.
- Quasi-Unimodal: This category relaxes the constraint of unimodal losses by allowing small variations while preserving ordinal coherence.
- Spatial: Spatial losses penalize semantic inconsistencies between neighboring pixels, encouraging smoother transitions in the image space.
In particular, this study adapts loss functions originally proposed for ordinal classification to ordinal semantic segmentation. Among the examined functions, the following have shown promising results in the context of medical imaging:
- Expanded Mean Squared Error (EXP_MSE): This function addresses the issue of pixel-level inconsistencies by integrating ordinal relationships into the mean squared error framework.
- Quasi-Unimodal Loss (QUL): This approach provides flexibility in class probability distributions while maintaining the ordinal structure, resulting in improved segmentation performance.
- Contact Surface Loss using Signal Distance Function (CSSDF): This spatial loss function emphasizes the importance of pixel interrelations, enhancing the smoothness of transitions in pixel classifications.
These innovative approaches have shown significant improvements in robustness, generalization, and anatomical consistency in medical imaging tasks. By effectively leveraging the ordinal relationships inherent in medical and odontological images, researchers and practitioners can achieve more accurate and reliable segmentation results.
The implementation of these loss functions opens up new avenues for enhancing the performance of deep learning models in various medical applications, including tumor detection, organ segmentation, and other critical diagnostic tasks. Furthermore, the adaptability of these methods to different imaging modalities underscores their versatility and potential impact on the field.
In conclusion, the integration of ordinal semantic segmentation techniques into medical and odontological imaging represents a significant step forward in the application of deep learning methodologies. As the research community continues to explore and refine these approaches, the prospects for improved patient outcomes and diagnostic accuracy remain promising.