{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MHLBUDPUJDI7N77B2VLHSZ2Z3M","short_pith_number":"pith:MHLBUDPU","schema_version":"1.0","canonical_sha256":"61d61a0df448d1f6ffe1d556796759db3a1e3a8cb96073f24a4fe5016d06a56b","source":{"kind":"arxiv","id":"1904.06890","version":1},"attestation_state":"computed","paper":{"title":"Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.CB"],"primary_cat":"cs.CV","authors_text":"Dennis Eschweiler, Johannes Stegmaier","submitted_at":"2019-04-15T07:45:47Z","abstract_excerpt":"The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids of the cell nuclei using a convolutional neural network (CNN) for the 2D images and a Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data sets. Tracking was performed in a backwards fashion on the predicted seed points, i.e., starting at the last frame and sequentially connecting corresponding objects until the first frame was reached. Correspondences were identified by propagating detections"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.06890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-15T07:45:47Z","cross_cats_sorted":["q-bio.CB"],"title_canon_sha256":"33f08e6188f117565d2bb136ad507349aae43c79b55a26e7ccef5f22617f1da1","abstract_canon_sha256":"18210b33f3c675a8a909af4be7aa4ba9538ddbf8ba4f980f87527c7821b73113"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:35.860694Z","signature_b64":"31b0nO36jFWtCgk6Sod9xl+/STWolo453TW/QkNFCvHsGPF2u9wXcX4F4wantoPtDbXH/v/5Msy8XjTeomewBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61d61a0df448d1f6ffe1d556796759db3a1e3a8cb96073f24a4fe5016d06a56b","last_reissued_at":"2026-05-17T23:48:35.860053Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:35.860053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.CB"],"primary_cat":"cs.CV","authors_text":"Dennis Eschweiler, Johannes Stegmaier","submitted_at":"2019-04-15T07:45:47Z","abstract_excerpt":"The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids of the cell nuclei using a convolutional neural network (CNN) for the 2D images and a Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data sets. Tracking was performed in a backwards fashion on the predicted seed points, i.e., starting at the last frame and sequentially connecting corresponding objects until the first frame was reached. Correspondences were identified by propagating detections"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06890","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.06890","created_at":"2026-05-17T23:48:35.860143+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.06890v1","created_at":"2026-05-17T23:48:35.860143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06890","created_at":"2026-05-17T23:48:35.860143+00:00"},{"alias_kind":"pith_short_12","alias_value":"MHLBUDPUJDI7","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"MHLBUDPUJDI7N77B","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"MHLBUDPU","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M","json":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M.json","graph_json":"https://pith.science/api/pith-number/MHLBUDPUJDI7N77B2VLHSZ2Z3M/graph.json","events_json":"https://pith.science/api/pith-number/MHLBUDPUJDI7N77B2VLHSZ2Z3M/events.json","paper":"https://pith.science/paper/MHLBUDPU"},"agent_actions":{"view_html":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M","download_json":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M.json","view_paper":"https://pith.science/paper/MHLBUDPU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.06890&json=true","fetch_graph":"https://pith.science/api/pith-number/MHLBUDPUJDI7N77B2VLHSZ2Z3M/graph.json","fetch_events":"https://pith.science/api/pith-number/MHLBUDPUJDI7N77B2VLHSZ2Z3M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M/action/storage_attestation","attest_author":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M/action/author_attestation","sign_citation":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M/action/citation_signature","submit_replication":"https://pith.science/pith/MHLBUDPUJDI7N77B2VLHSZ2Z3M/action/replication_record"}},"created_at":"2026-05-17T23:48:35.860143+00:00","updated_at":"2026-05-17T23:48:35.860143+00:00"}