Abstract
Structures to be analyzed in medical images are usually inaccessible. The sufficiency and correctness of employed domain knowledge cannot be proven. Hence, validation of an analysis method estimates the correctness of results from tests on a limited number of samples. For carrying out the validation suitable samples need to be selected, comparison measures have to be chosen that reflect the quality of the result, and a norm is required against which the method is tested. These aspects are realized differently for a delineation task, a detection task, or a registration task. Requirements, means, and limitations of validation will be discussed in this chapter.
Concepts, notions and definitions introduced in this chapter
- Overlap and outlier measures for delineation tasks: oversegmentation and undersegmentation, Dice and Jacard coefficient, Hausdorff distance
- The ROC curve
- Success in detection: type I and type II errors, sensitivity and specificity, precision and recall rates
- Measuring registration errors
- Ground truth: manual delineation, hardware and software phantoms
- Training and test data
- Significance: t-test and Welsh test
- The p-value
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Toennies, K.D. (2012). Validation. In: Guide to Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2751-2_13
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DOI: https://doi.org/10.1007/978-1-4471-2751-2_13
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