Post-processing#
Can we leverage the segmentation performance by post-processing?
Morphological operators
Dense conditional random field (CRF)
Manual user interaction (e.g., brushing)
Materials#
mialab.filtering.postprocessing
, e.g. useDenseCRF
P. Krähenbühl and V. Koltun, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Advances in Neural Information Processing Systems, vol. 24, pp. 109-117, 2011.
S. Nowozin and C. H. Lampert, Structured Learning and Prediction in Computer Vision, Foundations and Trends in Computer Graphics and Vision, vol. 6, pp. 185-365, 2010.
P. Cattin, Image Segmentation, 2016. [Online]. Available: https://www.miac.unibas.ch/SIP/pdf/SIP-07-Segmentation.pdf [Accessed: 08-Sep-2020], see chapter 6 - Mathematical Morphology
Evaluation#
Which metrics are suitable for our task? What is the influence of the validation procedure on the results?
Metric types
Influence of e.g. small structures
Influence of validation procedure
Materials#
See
pymia.evaluation
packageA. A. Taha and A. Hanbury, Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC Med. Imaging, vol. 15, no. 1, pp. 1–28, 2015.