Abstract
We present an object-level metric for segmentation performance which was developed to quantify both over- and under-segmentation errors, as well as to penalize segmentations with larger deviations in object shape. This metric is applied to the problem of segmentation of cell nuclei in routinely stained H&E histopathology imagery. We show the correspondence between the metric terms and qualitative observations of segmentation quality, particularly the presence of over- and under-segmentation. The computation of this metric does not require the use of any point-to-point or region-to-region correspondences but rather simple computations using the object mask from both the segmentation and ground truth.
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Boucheron, L.E., Harvey, N.R., Manjunath, B.S. (2007). A Quantitative Object-Level Metric for Segmentation Performance and Its Application to Cell Nuclei. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76858-6_21
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DOI: https://doi.org/10.1007/978-3-540-76858-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76857-9
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