Extensions to auto-context segmentation are proposed and applied to segmentation of multiple organs in porcine offal as a component of an envisaged system for post-mortem inspection at abbatoir. In common with multi-part segmentation of many biological objects, challenges include variations in configuration, orientation, shape, and appearance, as well as inter-part occlusion and missing parts. Auto-context uses context information about inferred class labels and can be effective in such settings. Whereas auto-context uses a fixed prior atlas, we describe an adaptive atlas method better suited to represent the multimodal distribution of segmentation maps. We also design integral context features to enhance context representation. These methods are evaluated on a dataset captured at abbatoir and compared to a method based on conditional random fields. Results demonstrate the appropriateness of auto-context and the beneficial effects of the proposed extensions for this application.
- Multi-class segmentation
- Atlas-based segmentation
- Automated inspection
McKenna, S., Amaral, T., Plotz, T., & Kyriazakis, I. (2018). Multi-part segmentation for porcine offal inspection with auto-context and adaptive atlases. Pattern Recognition Letters, 112, 290-296. https://doi.org/10.1016/j.patrec.2018.07.031