seg_wiring_variation — EMPROOF wiring op

• 資料種類:labels2d × labels2d × table → table

• 呼叫: import fullseye as fs; fs.ledger.seg_wiring_variation(a, b, synapses, spacing=None, background=0)(要直接呼叫實作,import segcompare; segcompare.seg_wiring_variation(a, b, synapses, spacing=None, background=0);從台帳取用則 opsemproof.get("seg_wiring_variation"))

用法

> 該運算子的說明尚無譯文,以下照原文給出。

How a candidate segmentation b corrupts the wiring diagram of truth a, in bits.

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.

詳細使用指南

• emproof 族使用指南

參考(範例資料、文獻)

• 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。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• poc_em_wiring_errors — py -3.11 examples/poc_em_wiring_errors.py

型別可銜接的下一個運算子(可接受 table 作為輸入)

seg_synapse_partners · seg_synapse_nri · seg_wiring_exposure

同類別(wiring)

seg_synapse_partners · seg_synapse_nri · 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.