Family of boundary overlap metrics for the evaluation of medical image segmentation

Journal of Medical Imaging
Varduhi Yeghiazaryan, Irina Voiculescu

Abstract

All medical image segmentation algorithms need to be validated and compared, yet no evaluation framework is widely accepted within the imaging community. None of the evaluation metrics that are popular in the literature are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, and shape) but no single metric covers all error types. We introduce a family of metrics, with hybrid characteristics. These metrics quantify the similarity or difference of segmented regions by considering their average overlap in fixed-size neighborhoods of points on the boundaries of those regions. Our metrics are more sensitive to combinations of segmentation error types than other metrics in the existing literature. We compare the metric performance on collections of segmentation results sourced from carefully compiled two-dimensional synthetic data and three-dimensional medical images. We show that our metrics: (1) penalize errors successfully, especially those around region boundaries; (2) give a low similarity score when existing metrics disagree, thus avoiding overly inflated scores; and (3) score segmentation results over a wider range of values. We analyze a...Continue Reading

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Citations

Jan 10, 2021·Scientific Reports·Erlend HodnelandIngfrid Haldorsen
Apr 13, 2021·Frontiers in Neurology·Augustin C OgierDavid Bendahan
May 13, 2021·Medical Physics·Yudi Sang, Dan Ruan
May 23, 2021·Computers in Biology and Medicine·Ying-Hwey NaiAnthonin Reilhac
Jul 18, 2021·BMC Medical Imaging·Orhun Utku AydinVince Istvan Madai
Aug 24, 2021·Computers in Biology and Medicine·Oldřich KodymAdam Herout

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