wiring op• 資料種類:labels2d × labels2d × table → table
• 呼叫: import fullseye as fs; fs.ledger.seg_synapse_nri(a, b, synapses, spacing=None, background=0)(要直接呼叫實作,import segcompare; segcompare.seg_synapse_nri(a, b, synapses, spacing=None, background=0);從台帳取用則 opsemproof.get("seg_synapse_nri"))
> 該運算子的說明尚無譯文,以下照原文給出。
Neural reconstruction integrity (Reilly et al. 2018) of candidate b against truth a.
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.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table 作為輸入)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
wiring)seg_synapse_partners · seg_wiring_variation · seg_wiring_exposure
*Provenance: segcompare.py — EMPROOF 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.