wiring op• Data kinds: labels2d × labels2d × table → table
• Call: import fullseye as fs; fs.ledger.seg_wiring_variation(a, b, synapses, spacing=None, background=0) (implementation को सीधे call करना हो तो import segcompare; segcompare.seg_wiring_variation(a, b, synapses, spacing=None, background=0), registry से लेना हो तो opsemproof.get("seg_wiring_variation"))
candidate segmentation b truth a के wiring diagram को कितना बिगाड़ता है, bit में।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Two levels, both VOI split / merge (synapses as in seg_synapse_partners):
• synapse ends (split / merge, voi): the segmentation VOI measured only at the 2n synapse ends instead of every pixel. split = two ends on one true neuron now on different objects (the neuron was cut between its synapses); merge = ends on different true neurons now on one object (they were glued). Cutting a neuron whose s ends divide s1 / s2 raises split by exactly (s / 2n)·H2(s1 / s). Pixels far from any synapse cost nothing here.
• connections (connection_split / connection_merge): synapses grouped by their (pre object, post object) pair — one true connection scattered over several, or different true connections fused into one (synapses attributed to the wrong partner). A connection with a single synapse cannot scatter, so this level is blind to cuts that only rename a one-synapse connection; the ends level is not.
Provenance: the ends level is the "synapse VI" of Plaza, Scheffer & Chklovskii 2014 (*Focused proofreading*, arXiv:1409.1199; VI over the voxels carrying synaptic annotations, implemented in NeuroProof, C++). The connection level, the lost-synapse convention and the per-edit closed forms ((s/2n)·H2(s1/s) for a cut) are this module's. Synapses with an end on background in the truth are dropped; in the candidate, each such synapse is lost and counted in n_lost as its own singleton at both levels (not one shared "background" object, which would fake a huge merge). Also returns split_connections (true connections spread over 2+ candidate ones), merged_connections (candidate connections holding synapses of 2+ true ones), edges_a / edges_b and n (synapses scored).
Raises ValueError: as seg_synapse_partners; shapes of a and b differ; no synapse left after dropping background in the truth.
• 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_synapse_nri · seg_wiring_exposure
wiring)seg_synapse_partners · seg_synapse_nri · 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.