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. 2015 Nov 5;9:142. doi: 10.3389/fnana.2015.00142

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Copyright © 2015 Arganda-Carreras, Turaga, Berger, Cireşan, Giusti, Gambardella, Schmidhuber, Laptev, Dwivedi, Buhmann, Liu, Seyedhosseini, Tasdizen, Kamentsky, Burget, Uher, Tan, Sun, Pham, Bas, Uzunbas, Cardona, Schindelin and Seung.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Metric robustness to thinning. (A) Rand (V^Rand) and information theoretic (V^Info) scoring measures produce similar rankings, Spearman correlation ρ = 0.80. (B) This correlation is greatly increased by post-processing boundaries by thinning, Spearman correlation ρ = 0.94. (C,D) Thinning of boundaries almost universally improves Rand and information theoretic scoring measures. V^Rand rankings are more robust to thinning, (C) Spearman correlation ρ = 0.89, compared to V^Info rankings, (D) Spearman correlation ρ = 0.59.