wiring op• Data kinds: labels2d × labels2d × table → table
• Call: import fullseye as fs; fs.ledger.seg_synapse_nri(a, b, synapses, spacing=None, background=0) (implementation को सीधे call करना हो तो import segcompare; segcompare.seg_synapse_nri(a, b, synapses, spacing=None, background=0), registry से लेना हो तो opsemproof.get("seg_synapse_nri"))
truth a के सापेक्ष candidate b की neural reconstruction integrity (Reilly et al. 2018)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py
table को input के रूप में लेते हैं)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. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.