{"as_of":"2026-08-21T09:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1dd18441ba52045eb7eedc24cd9552bc5d9534cc114a2f93f4986d7a7fde0a23","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-15T04:56:33.261444Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.12586/citation-record","integrity":"/paper/2607.12586/integrity","json":"/paper/2607.12586/citation-record.json","paper":"/paper/2607.12586"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/1.ap.7.5.054002","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep learning for computational imaging: from data-driven to physics-enhanced approaches","venue":"Advanced Photonics","work_id":"89d6edc2-9bc5-457d-a1e6-87ed3de45127","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:2c344defe8c43168cf172ee5be3b326952028cca521675b941b8e92921abc961","observation_id":"8f8add10-ff56-4757-97cb-195796aa9922","resolution":{"observed_at":"2026-07-15T05:00:59.335277Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:49:57.415817+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:49:57.415817+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Ronneberger, P","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:0ca019745e720bc5d2933f3a9d45330a77fa26250fb3a83873d191ffacadfbbf","observation_id":"a826c64e-274c-4179-afe3-3eb80f326df5","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Badrinarayanan, A","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:66ea8a33f2e3082d5ab718af230e09d1664dd19539d8b59bb72d3bd46d3c86f9","observation_id":"93cdb0d6-5522-48bb-8c7b-420b90cd42d1","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"A comprehensive review of convolutional neural networks: architectures, training methods, and recent advances","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:90f034a8ff25c9fe37a12c7d6290d02abd5c1cfc0bce9cc27193439dde980bd2","observation_id":"1bb74d0c-6b5d-45a1-841e-7b4a0bcb65ef","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10462-020-09825-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T05:00:59.252055Z","title":"A survey of the recent architectures of deep convolutional neural networks","venue":null,"work_id":"65c9cebc-99b5-446d-aa12-ba4c5d08a8ec","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:a9523f3254dfd26a8742aaddcdae3a1ce509780d8d761a844bb79229b92bda3a","observation_id":"385e53bb-c857-4068-a4f5-3ee2670386d8","resolution":{"observed_at":"2026-07-15T05:00:59.254891Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-032-09513-8_7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regional Hausdorff distance losses for medical image segmentation","venue":"Lecture notes in computer science","work_id":"c9960d7e-1cb3-4d8a-ba7f-210a061dd76b","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:ec74d6b5331c68a317339f923d94bdbcd637b512381bcacf6d05cc59cdee22e9","observation_id":"df5aaae5-c3f6-4d2b-ba1e-5db179b6bff5","resolution":{"observed_at":"2026-07-15T05:00:59.322832Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:49:58.448465+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:49:58.448465+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.103612","doi":"10.1016/j.media.2025.103612","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MedMamba-UNet: pure Mamba-based U-shaped architecture for efficient medical image segmentation","venue":"Medical Image Analysis","work_id":"6a409765-df19-433b-9552-625037742573","year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:6181a2556ff2264418610e5e7242ab06712039626d15be9923c9fe224f395432","observation_id":"12955a54-f3af-43d6-91b4-cf36b2be9001","resolution":{"observed_at":"2026-07-15T05:00:59.250503Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:49:58.902511+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:49:58.902511+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s23041887","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A new regularization for deep learning-based segmentation of images with fine structures and low contrast","venue":"Sensors","work_id":"62e000ab-8724-401d-9d1b-5df8d0839c12","year":2023},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:abb8b0d05fceee673d34d7ca8d1e23d8420782d51793067e47621410527c8d4c","observation_id":"891be8ff-47ec-49aa-ae1a-212412f01d8d","resolution":{"observed_at":"2026-07-15T05:00:59.297177Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:49:59.440276+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:49:59.440276+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.112613","doi":"10.1016/j.patcog.2025.112613","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Spatially continuous dual optimization on compactness function for image segmentation","venue":"Pattern Recognition","work_id":"47a3beb6-2755-411d-a5f2-7e5f3f12500b","year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:67662870f23116c1b8590404dd733f387954df239d112c71c7d48db66fd24093","observation_id":"eb93c39d-5129-47e9-b863-5366df8c1bcc","resolution":{"observed_at":"2026-07-15T05:00:59.288625Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:49:59.899499+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:49:59.899499+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"ITSRS: an inverse Taylor series adaptive loss based on synergized regional-structural information for medical image segmentation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:e283069fff408fd7040c6e400296e7d0ffc1d154162b4eee0eaf62b41da76789","observation_id":"2e297dc2-daa5-4618-8f3a-d6fb31a96b68","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09274","last_updated":"2025-05-14T10:48:37Z","snapshot_observed_at":"2026-08-19T17:44:41.654191Z","submitted_at":"2025-05-14T10:48:37Z","title":"Recent Advances in Medical Imaging Segmentation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09274","snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Recent advances in medical imaging segmentation: a survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"cited_paper":"/paper/2505.09274","citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:2a659a61bf462a2f64df2730194d9e4ef256ca66bd30959088d8d09cc6079ab3","observation_id":"4f326883-9392-4acb-a4bd-3582731669cd","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Proximal splitting algorithms for convex optimization: a tour of recent advances, with new twists","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:3b0d61bfb16257b77b0e9d7b2f7b233128a9da7ee887dca63f4d287baf6228c2","observation_id":"c34cad4c-dcf2-471c-ac31-b0bce1edd609","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"A comprehensive survey of loss functions and metrics in deep learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:5213e5416eb0fad89fa53655643383d0d6afbb83eb6b6ddca8536078ba86b3fe","observation_id":"9ef49679-3b4e-4661-a851-33e6ea54328c","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Topology-preserving image segmentation with spatial-aware feature learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:f86506337174010e8dacb6c60fc4be6dce3c6e3c9596b7885e4ac8b8c365b3f8","observation_id":"08677090-1b97-4814-af59-1a3c109a8434","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.129395","doi":"10.1016/j.neucom.2025.129395","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep convolutional neural networks meet variational shape compactness priors for image segmentation","venue":"Neurocomputing","work_id":"2843ee93-07d8-4049-a73b-0c114b28606a","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:499329f3187579662344bf3fd332578a2566302b986c9e67a2d57c27019838f2","observation_id":"4996426f-a67d-4063-92ca-1523b6b26c43","resolution":{"observed_at":"2026-07-15T05:00:59.332195Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:00.8656+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:00.8656+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.105833","doi":"10.1016/j.imavis.2025.105833","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Semi-supervised medical image segmentation via anatomy-preserving consistency training","venue":"Image and Vision Computing","work_id":"0607cc88-90ba-4122-8b5e-3c4728905c4b","year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:27be86fe2e605d57f280c5f9533819f38c4ae3d4aa2509665b1f367c98d7ba11","observation_id":"5688bfd3-699e-487f-9074-014ba2fe3d3d","resolution":{"observed_at":"2026-07-15T05:00:59.344734Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:01.337054+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:01.337054+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05473","snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"LMS-Net: a learned Mumford-Shah network for few-shot medical image segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"cited_paper":"/paper/2502.05473","citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:1b738f7166c7b2700cf83cd36c6b5714fb9fbb28d95905df0bd8a09dd3da9b9a","observation_id":"c99bea82-1a0c-454c-a30e-32ea18d08591","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Diff-SegNet: diffusion-guided encoder-decoder network for uncertain region refinement in medical image segmentation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:75d27b0e58ec477c5efa46be1f4fadf656379ce60a8402090f1f51d55ad65815","observation_id":"2f985809-e7ba-4f00-bac8-a8021d8a2eee","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"EfficientMedNeXt: multi-receptive dilated convolutions for medical image segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:4834e60ee328c598b37694b0e7b86f6f84470d4460925ca7fe7c27c0c3027661","observation_id":"85862da2-505b-49ea-9f41-85b0535336da","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11760-026-05378-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A hybrid framework integrating active contour and deep learning for optic disc and optic cup segmentation","venue":"Signal Image and Video Processing","work_id":"aad499ac-8780-401b-ad1e-151e6f710f1b","year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:c77bae4df2f2bf43abf859bd46420ba148bd80c95aeb05a0346787b4adaa3899","observation_id":"cb846f0c-e778-43f9-8d5c-09ffd2a1f20c","resolution":{"observed_at":"2026-07-15T05:00:59.319309Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:01.830882+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:01.830882+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"A novel deep neural architecture for efficient and scalable multi-domain image classification","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:ff3d438530f40697aa99af1c278f0b980093936ae188293f305a45933d50ad16","observation_id":"ebc98625-3b4f-4fa2-a471-3316cd356ddd","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10278-025-01387-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GLAC-UNet: global-local active contour loss with an efficient U-shaped architecture for multiclass medical image segmentation","venue":"Journal of Imaging Informatics in Medicine","work_id":"0cf0671b-694d-4ebb-90c1-812cd50c5ad5","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:17192380d0af526156b6cda637a1d0c6d793f101bda519c62e44848c972ca3e4","observation_id":"6c021c63-45c0-4b4c-b65a-dc836f86aabb","resolution":{"observed_at":"2026-07-15T05:00:59.339372Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:02.443892+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:02.443892+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s25092864","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-driven active contour (CDAC): a novel medical image segmentation method based on active contour and contextual understanding","venue":"Sensors","work_id":"60e5de38-5973-46e9-a1e2-6b6378532f25","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:251967069619b7d49f6e08c11732874e89f73d4210992a0e00a56a37ad9a716f","observation_id":"03c00511-f5fd-4fde-b730-30f5300f611a","resolution":{"observed_at":"2026-07-15T05:00:59.261799Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:02.967796+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:02.967796+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/25m1765870","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Topology-guaranteed image segmentation: enforcing connectivity, genus, and width constraints","venue":"SIAM Journal on Imaging Sciences","work_id":"8d7028bf-afab-422d-911a-3c050a9ba16d","year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:97f054b59cbfcfcedd39be7b51d9b259f2e0e03987d68f00e7bde5087a967810","observation_id":"253dea79-7c63-45a9-912b-f7000d76ca39","resolution":{"observed_at":"2026-07-15T05:00:59.281321Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:03.312922+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:03.312922+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.108742","doi":"10.1016/j.compbiomed.2025.108742","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T06:15:00.888325Z","title":"CurvDrop: data-efficient learning for medical image segmentation via curvature-based sample selection","venue":null,"work_id":"40d2a22a-3be5-43d4-9d38-975b44b0511d","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:e94a7ff9b47256983993959bb179e9b300c7fa49c9ffa0c5eecc478ff41b06ad","observation_id":"38322617-6fda-4f88-a59b-6d2d3f36e0a1","resolution":{"observed_at":"2026-07-15T05:00:59.309563Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:03.638321+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:03.638321+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Optimal approximations by piecewise smooth functions and associated variational problems","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:e0f71c3a6b8ff537e2aefb1b95dc6f96c08429d4856c45a9df6cb352767c4f27","observation_id":"cb1dcb7c-7a1e-4e10-b8d8-b0f78d83156c","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"The Potts model with different piecewise constant representations and fast algorithms: a survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:7677a7325fd7de34409f152d3658986e45b1ed97e05ee73b48ddf3e0960df36b","observation_id":"2f902659-e168-494d-ab6f-ee8a98dad7a5","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-030-98661-2_90","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"7b379e69-4c66-450e-b76e-d93f4b4223dc","year":null},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:c52b2e64ed1fe525e0f0892cc22c920716e506b066f185e17515f31645543951","observation_id":"d926f82b-156e-4f50-8aa8-18a87441c621","resolution":{"observed_at":"2026-07-15T05:00:59.271766Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:04.109402+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:04.109402+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Oktay, J","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:3250f26976ea1c95dee1497f113107e03e5fef066c5695f35c94286e8e19dffc","observation_id":"560dab47-a15a-4389-8baa-f61a33e6b03c","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00526","last_updated":"2020-11-01T15:14:37Z","snapshot_observed_at":"2026-08-20T08:00:44.325504Z","submitted_at":"2020-11-01T15:14:37Z","title":"Learning Euler's Elastica Model for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.00526","snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Learning Euler's elastica model for medical image segmentation","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"cited_paper":"/paper/2011.00526","citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:f16e63bcad2706bc840bfba55e5a0f7288e974844ad366f65ba9b36b51d7179b","observation_id":"ce2d4883-ee91-4fd1-a801-6b3bf319f443","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Robust variational model based tailored UNet: leveraging edge detector and mean curvature for improved image segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:4ac4039edafc91dbe8cdfc5da7f7521df62d58e84c32fceb5a352adbf3dd16e8","observation_id":"5f634788-364b-4031-be48-dba8a690a0e5","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Discriminative curvature regularization loss for boundary segmentation in microscopy cell images","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:dedd7979c4efba6e4f8b6154f4bf8a68b5b3104670bb32c5708df152d2f8be1e","observation_id":"bd295888-965d-4405-81da-78aa75901b85","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.20918","last_updated":"2026-04-22T03:54:47Z","snapshot_observed_at":"2026-07-06T23:07:36.996629Z","submitted_at":"2026-04-22T03:54:47Z","title":"EDU-Net: Retinal Pathological Fluid Segmentation in OCT Images with Multiscale Feature Fusion and Boundary Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.20918","snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"EDU-Net: retinal pathological fluid segmentation in OCT images with multiscale feature fusion and boundary optimization","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"cited_paper":"/paper/2604.20918","citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:8628263bdd7bf7b7374b99e3ea1bf415dfc4ee7c81771414c6ce08c0f49e07fb","observation_id":"14abeb5d-675c-40f3-a382-e3e913a0b935","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/24m1705811","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T05:00:59.324858Z","title":"A median filter scheme for mean curvature flow","venue":null,"work_id":"2370f148-cf1c-467f-a5db-8077d59e6501","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:b08559312424d60ac758be8a3979a30ac615ada0998cb3c5ddef47299455f937","observation_id":"808ecb58-14d2-4b9d-aa08-bc3221381065","resolution":{"observed_at":"2026-07-15T05:00:59.326695Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:04.438075+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:04.438075+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"Active contour models driven by hyperbolic mean curvature flow for image segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:c702b46fc3a52efe2b0a55e2403fc9c9ce2877e09834d7784d4421a8f25f6d95","observation_id":"2a836747-72e2-4f52-867c-b0805d36a48d","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-89576-0_5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Variational and PDE-based static and video image segmentation","venue":"Studies in computational intelligence","work_id":"bb46e446-9e97-40bd-b14b-fdc44fa4640f","year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:23627dd5a458cc82006f49773f1d82873de080ea84b3c214a1ec3ceaf85bf091","observation_id":"1f25731e-6af9-4c51-83c0-0c84a4e9ba92","resolution":{"observed_at":"2026-07-15T05:00:59.313296Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-07-15T08:50:04.867912+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T08:50:04.867912+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"CHAOS challenge: combined (CT- MR) healthy abdominal organ segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:c2c8d2c876e33a20d984dff8567fc1d94b9aaf3aaf839ece352a3f9b2075fb4c","observation_id":"6041ce3e-f38c-4871-9e84-560e4ccfc0dd","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:c4ae74143860f1ea39d657e63d3e2d088df5ddfefdcea4f467dfcbec9201d117","observation_id":"dbe3f4bc-8a42-41fc-9cf9-1baa95a0380b","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":15,"verified_fuzzy":0},"total_outbound_references":38},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.12586."}