Publication #319

A Quantitative Object-Level Metric for Segmentation Performance and Its Application to Cell Nuclei

Laura E. Boucheron 1,2, Neal R. Harvey 2, and B.S. Manjunath 1
1 University of California Santa Barbara, Electrical and Computer Engineering,
Santa Barbara, CA 93106-9560
2 Los Alamos National Laboratory, Space and Remote Sensing Sciences, P.O. Box
1663, Los Alamos, NM 87545

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.

[PDF] [BibTex]

Laura E. Boucheron, Neal R. Harvey and B.S. Manjunath,
"A Quantitative Object-Level Metric for Segmentation Performance and Its Application to Cell Nuclei"
Proc. International Symposium on Visual Computing (ISVC), Lake Tahoe, Nevada/California, pp. 208–219, Nov. 2007.

DB ID: 319, Lab: VRL, Target: Proceedings, Status: Printed
Subject: [Object-Based_Retrieval] « Look-up more
Project: [ITR] « Look-up more

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