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Semantic segmentation - Dice Coefficient, Precision, Recall and Specificity

Semantic segmentation - Dice Coefficient, Precision, Recall and Specificity

Improve your understanding of quantitative results in semantic segmentation.

Summary:

  • Dice Coefficient: The closer the value is to 1,

  • Precision [%]: The closer the value is to 100%,

  • Recall [%]: The closer the value is to 100%,

  • Specificity: The closer the value is to 100%,

the better your trained application performs.

What to expect from this page?

Basic theory

Metrics for semantic segmentation are calculated per label based on the so-called confusion matrix. It means that each pixel of an image or ROI is classified into one of four categories about the result of the application training (the ‘prediction’ or ‘predicted areas’).

The confusion matrix is unique for each label, and it contains the count of annotation (ground truth) cases and predicted pixel labels, i.e. true positives, false positives, true negatives, and false negatives. These metrics are used to evaluate the correctness of the overall app performance.

All metrics are computed unweighted for each ROI and/or image, and as a total for each label in a bottom-up fashion and don't represent aggregations, such as averages or median values.

Please note: In the results of the application training presented in the PDF report and the visualizations, we do not include true-negative predicted areas (to avoid visual confusion).