Date: 28 Jul 2007

Statistical validation metric for accuracy assessment in medical image segmentation

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Abstract

Objective Validation of medical image segmentation algorithms is an open question, considering variance of individual pathologies and the related clinical requirements for accuracy. In this paper, we propose a validation metric capable to distinguish between an over and under-segmentation and account for different clinical applications.

Materials and methods In this paper, we propose a validation metric representing a tradeoff between sensitivity and specificity. The metric has an advantage of differentiating between an over or under-segmentation which is an important feature for validating large sets of segmentation results, as human inspection is exhausting and time consuming. Although it is oriented to the accuracy measurement it is also closely related to the robustness of a method.

Results Features of the metrics are analyzed alongside their medical impact. A set of numerical simulations is performed in order to compare the proposed metric with standardly used discrepancy measures. The metric is illustrated with a clinical case study, presenting accuracy assessment of an algorithm for calvarial tumor segmentation, validated on six patients.