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生物影像分析中的分割度量误读。

Segmentation metric misinterpretations in bioimage analysis.

机构信息

Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.

Doctoral School of Computer Science, University of Szeged, Szeged, Hungary.

出版信息

Nat Methods. 2024 Feb;21(2):213-216. doi: 10.1038/s41592-023-01942-8. Epub 2023 Jul 27.

Abstract

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.

摘要

图像分割算法的定量评估在生物图像分析领域至关重要。然而,最常见的评估分数往往被误解,并且多个定义具有相同的名称。在这里,我们展示了分割算法评估指标的歧义,并展示了这些误解如何改变有影响力的竞赛排行榜。我们还提出了如何解决当前存在问题的指导方针。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c7e/10864175/bc38e2a1bf2e/41592_2023_1942_Fig1_HTML.jpg

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