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. use DenseCRF

  • 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 package

  • A. 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.

  • Cross-validation in machine learning