{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:V2LU7AWUYNL5YJBUGSECUDDGCY","short_pith_number":"pith:V2LU7AWU","canonical_record":{"source":{"id":"1610.09032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-27T23:23:56Z","cross_cats_sorted":[],"title_canon_sha256":"8c043f820538ddc11ad441790d7600c26aee016ee962973c38b8542bf1b08e7b","abstract_canon_sha256":"c8a54a98ccd2199e1710db3d92204753e848f65ba7d427686f1ee2c999bf9a3e"},"schema_version":"1.0"},"canonical_sha256":"ae974f82d4c357dc243434882a0c661637ce01bd48e997ad3154fba8fcece3df","source":{"kind":"arxiv","id":"1610.09032","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.09032","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"arxiv_version","alias_value":"1610.09032v1","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.09032","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"pith_short_12","alias_value":"V2LU7AWUYNL5","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"V2LU7AWUYNL5YJBU","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"V2LU7AWU","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:V2LU7AWUYNL5YJBUGSECUDDGCY","target":"record","payload":{"canonical_record":{"source":{"id":"1610.09032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-27T23:23:56Z","cross_cats_sorted":[],"title_canon_sha256":"8c043f820538ddc11ad441790d7600c26aee016ee962973c38b8542bf1b08e7b","abstract_canon_sha256":"c8a54a98ccd2199e1710db3d92204753e848f65ba7d427686f1ee2c999bf9a3e"},"schema_version":"1.0"},"canonical_sha256":"ae974f82d4c357dc243434882a0c661637ce01bd48e997ad3154fba8fcece3df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:59.388944Z","signature_b64":"dutNoSgT19p0pb/xwWrUcr+fmZu6CXA+lgx+uAxkZd0tl4iOq9f4sGoRCP3WItUhgOHSIaeMdveawDbxJhumDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae974f82d4c357dc243434882a0c661637ce01bd48e997ad3154fba8fcece3df","last_reissued_at":"2026-05-18T01:00:59.388350Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:59.388350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.09032","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MRu4bA3iUANWtAi7ScLqsh0am1oCDI+/yuXyLjQFEhj7hiEvdK3p18b9V8EVedL6F3WoV4zL55AcEFBTUsAEAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T15:45:43.970300Z"},"content_sha256":"035b15e3ee53fd005181a2f725977a65737702684d2be30b5457f62510828ceb","schema_version":"1.0","event_id":"sha256:035b15e3ee53fd005181a2f725977a65737702684d2be30b5457f62510828ceb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:V2LU7AWUYNL5YJBUGSECUDDGCY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daniel Haehn, Felix Gonda, Hanspeter Pfister, Jeff Lichtman, Ray Thouis, Toufiq Parag, Verena Kaynig","submitted_at":"2016-10-27T23:23:56Z","abstract_excerpt":"We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely tedious manual annotation task that is typically required for deep networks to perform well on image segmentation problems. Our proposed method employs a feedback loop that captures sparse annotations using a graphical user interface, trains a deep neural network based on recent and past annotations, and displays the prediction output to users in almost real-time. Our implementation of the algorithm also allows mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.09032","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X/VNP61RDGgDgUaL/NqlwRJKSxcu0PDtwWyscMREY8U4OrEtITJTp3dsfTlbCFu5QTKaJ/3utjZ5YNBHOMISCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T15:45:43.970921Z"},"content_sha256":"6585339634ba4a83c16b3880c0dea5d011ae7d304e48cef53dfe6e696281a7c1","schema_version":"1.0","event_id":"sha256:6585339634ba4a83c16b3880c0dea5d011ae7d304e48cef53dfe6e696281a7c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/bundle.json","state_url":"https://pith.science/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T15:45:43Z","links":{"resolver":"https://pith.science/pith/V2LU7AWUYNL5YJBUGSECUDDGCY","bundle":"https://pith.science/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/bundle.json","state":"https://pith.science/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V2LU7AWUYNL5YJBUGSECUDDGCY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:V2LU7AWUYNL5YJBUGSECUDDGCY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c8a54a98ccd2199e1710db3d92204753e848f65ba7d427686f1ee2c999bf9a3e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-27T23:23:56Z","title_canon_sha256":"8c043f820538ddc11ad441790d7600c26aee016ee962973c38b8542bf1b08e7b"},"schema_version":"1.0","source":{"id":"1610.09032","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.09032","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"arxiv_version","alias_value":"1610.09032v1","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.09032","created_at":"2026-05-18T01:00:59Z"},{"alias_kind":"pith_short_12","alias_value":"V2LU7AWUYNL5","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"V2LU7AWUYNL5YJBU","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"V2LU7AWU","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:6585339634ba4a83c16b3880c0dea5d011ae7d304e48cef53dfe6e696281a7c1","target":"graph","created_at":"2026-05-18T01:00:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely tedious manual annotation task that is typically required for deep networks to perform well on image segmentation problems. Our proposed method employs a feedback loop that captures sparse annotations using a graphical user interface, trains a deep neural network based on recent and past annotations, and displays the prediction output to users in almost real-time. Our implementation of the algorithm also allows mult","authors_text":"Daniel Haehn, Felix Gonda, Hanspeter Pfister, Jeff Lichtman, Ray Thouis, Toufiq Parag, Verena Kaynig","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-27T23:23:56Z","title":"Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.09032","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:035b15e3ee53fd005181a2f725977a65737702684d2be30b5457f62510828ceb","target":"record","created_at":"2026-05-18T01:00:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c8a54a98ccd2199e1710db3d92204753e848f65ba7d427686f1ee2c999bf9a3e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-27T23:23:56Z","title_canon_sha256":"8c043f820538ddc11ad441790d7600c26aee016ee962973c38b8542bf1b08e7b"},"schema_version":"1.0","source":{"id":"1610.09032","kind":"arxiv","version":1}},"canonical_sha256":"ae974f82d4c357dc243434882a0c661637ce01bd48e997ad3154fba8fcece3df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae974f82d4c357dc243434882a0c661637ce01bd48e997ad3154fba8fcece3df","first_computed_at":"2026-05-18T01:00:59.388350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:59.388350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dutNoSgT19p0pb/xwWrUcr+fmZu6CXA+lgx+uAxkZd0tl4iOq9f4sGoRCP3WItUhgOHSIaeMdveawDbxJhumDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:59.388944Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.09032","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:035b15e3ee53fd005181a2f725977a65737702684d2be30b5457f62510828ceb","sha256:6585339634ba4a83c16b3880c0dea5d011ae7d304e48cef53dfe6e696281a7c1"],"state_sha256":"da7398efae55e665394863c7fb1e57ee0dfd87cfa86f6452931b2e537d21962a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"acrTLVPaACBOGZVdMAuZCoUAmbdRCuPXRWbwIkv4ls2wUTY97J6J67Y5qz6xpvX6nfjEnWRoWTqAdqmV3PGbDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T15:45:43.974148Z","bundle_sha256":"9bcba73f736b465c53bb7f43bd0da81c03654d97927ffaf319781da2df5059dc"}}