wiring op• データ種: labels2d × labels2d × table → table
• 呼び出し: import fullseye as fs; fs.ledger.seg_synapse_nri(a, b, synapses, spacing=None, background=0) (実装を直接呼ぶなら import segcompare; segcompare.seg_synapse_nri(a, b, synapses, spacing=None, background=0)、台帳から引くなら opsemproof.get("seg_synapse_nri"))
NRI(Reilly 2018): シナプスの端の対のうち、正解でも候補でも同じ物体にある対の F 値。端の上の adapted Rand error の 1 − と厳密に一致することを内部で検査する。
> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。
NRI scores the pairs of synapse ends that a reconstruction keeps together. Over the 2n ends (synapses as in seg_synapse_partners; ends on background in the truth are dropped, ends on background in the candidate are singletons), a pair of ends that lies in one truth neuron and one candidate object is a true positive; a pair in one truth neuron but different candidate objects is a false negative (a split); a pair in one candidate object but different truth neurons is a false positive (a merge). nri = 2TP / (2TP + FP + FN) with the pair precision and recall.
Identity (checked inside, fail-closed): with n_ij the contingency table of the ends, TP = sum C(n_ij, 2), TP + FN = sum C(s_i, 2), TP + FP = sum C(t_j, 2), so NRI is exactly 1 - adapted_rand_error of seg_rand taken over the ends (the - N form of the pair counts is 2 C(n, 2)). Both are computed and must agree to 1e-12. Returns nri, precision, recall, tp, fp, fn, n_ends.
Raises ValueError: as seg_wiring_variation; fewer than 2 ends.
• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table を入力に取れる)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
wiring)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
*Provenance: segcompare.py — EMPROOF operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.