A quantitative object-level metric for segmentation performance and its application to cell nuclei

Authors:
Laura E. Boucheron;Neal R. Harvey;B. S. Manjunath
Affiliations:
University of California Santa Barbara, Electrical and Computer Engineering, Santa Barbara, CA and Los Alamos National Laboratory, Space and Remote Sensing Sciences, Los Alamos, NM;Los Alamos National Laboratory, Space and Remote Sensing Sciences, Los Alamos, NM;University of California Santa Barbara, Electrical and Computer Engineering, Santa Barbara, CA
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Year:
2007

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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.