Paper Citation Record · LEDGER

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.12586.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.12586 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T04:56:33.261444Z

measured 38 of 38 standing notices

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measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 8f8add10-ff56-4757-97cb-195796aa9922 · outbound

This paper cites Deep learning for computational imaging: from data-driven to physics-enhanced approaches.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Deep learning for computational imaging: from data-driven to physics-enhanced approaches

Reference 1

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.335277Z

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Observation a826c64e-274c-4179-afe3-3eb80f326df5 · outbound

This paper cites Ronneberger, P.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Ronneberger, P

Reference 2

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Observation 93cdb0d6-5522-48bb-8c7b-420b90cd42d1 · outbound

This paper cites Badrinarayanan, A.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Badrinarayanan, A

Reference 3

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Observation 1bb74d0c-6b5d-45a1-841e-7b4a0bcb65ef · outbound

This paper cites A comprehensive review of convolutional neural networks: architectures, training methods, and recent advances.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A comprehensive review of convolutional neural networks: architectures, training methods, and recent advances

Reference 4

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Observation 385e53bb-c857-4068-a4f5-3ee2670386d8 · outbound

This paper cites A survey of the recent architectures of deep convolutional neural networks.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A survey of the recent architectures of deep convolutional neural networks

Reference 5

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Observation df5aaae5-c3f6-4d2b-ba1e-5db179b6bff5 · outbound

This paper cites Regional Hausdorff distance losses for medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Regional Hausdorff distance losses for medical image segmentation

Reference 6

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.322832Z

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Observation 12955a54-f3af-43d6-91b4-cf36b2be9001 · outbound

This paper cites MedMamba-UNet: pure Mamba-based U-shaped architecture for efficient medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function MedMamba-UNet: pure Mamba-based U-shaped architecture for efficient medical image segmentation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-15T05:00:59.250503Z

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Observation 891be8ff-47ec-49aa-ae1a-212412f01d8d · outbound

This paper cites A new regularization for deep learning-based segmentation of images with fine structures and low contrast.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A new regularization for deep learning-based segmentation of images with fine structures and low contrast

Reference 8

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.297177Z

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Observation eb93c39d-5129-47e9-b863-5366df8c1bcc · outbound

This paper cites Spatially continuous dual optimization on compactness function for image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Spatially continuous dual optimization on compactness function for image segmentation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-15T05:00:59.288625Z

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Observation 2e297dc2-daa5-4618-8f3a-d6fb31a96b68 · outbound

This paper cites ITSRS: an inverse Taylor series adaptive loss based on synergized regional-structural information for medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function ITSRS: an inverse Taylor series adaptive loss based on synergized regional-structural information for medical image segmentation

Reference 10

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Observation 4f326883-9392-4acb-a4bd-3582731669cd · outbound

This paper cites Recent Advances in Medical Imaging Segmentation: A Survey.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Recent Advances in Medical Imaging Segmentation: A Survey

Reference 11

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Observation c34cad4c-dcf2-471c-ac31-b0bce1edd609 · outbound

This paper cites Proximal splitting algorithms for convex optimization: a tour of recent advances, with new twists.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Proximal splitting algorithms for convex optimization: a tour of recent advances, with new twists

Reference 12

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Observation 9ef49679-3b4e-4661-a851-33e6ea54328c · outbound

This paper cites A comprehensive survey of loss functions and metrics in deep learning.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A comprehensive survey of loss functions and metrics in deep learning

Reference 13

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Observation 08677090-1b97-4814-af59-1a3c109a8434 · outbound

This paper cites Topology-preserving image segmentation with spatial-aware feature learning.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Topology-preserving image segmentation with spatial-aware feature learning

Reference 14

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Observation 4996426f-a67d-4063-92ca-1523b6b26c43 · outbound

This paper cites Deep convolutional neural networks meet variational shape compactness priors for image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Deep convolutional neural networks meet variational shape compactness priors for image segmentation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-15T05:00:59.332195Z

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Observation 5688bfd3-699e-487f-9074-014ba2fe3d3d · outbound

This paper cites Semi-supervised medical image segmentation via anatomy-preserving consistency training.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Semi-supervised medical image segmentation via anatomy-preserving consistency training

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-15T05:00:59.344734Z

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Observation c99bea82-1a0c-454c-a30e-32ea18d08591 · outbound

This paper cites LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-15T04:56:33.261444Z

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Observation 2f985809-e7ba-4f00-bac8-a8021d8a2eee · outbound

This paper cites Diff-SegNet: diffusion-guided encoder-decoder network for uncertain region refinement in medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Diff-SegNet: diffusion-guided encoder-decoder network for uncertain region refinement in medical image segmentation

Reference 18

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Observation 85862da2-505b-49ea-9f41-85b0535336da · outbound

This paper cites EfficientMedNeXt: multi-receptive dilated convolutions for medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function EfficientMedNeXt: multi-receptive dilated convolutions for medical image segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-15T04:56:33.261444Z

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Observation cb846f0c-e778-43f9-8d5c-09ffd2a1f20c · outbound

This paper cites A hybrid framework integrating active contour and deep learning for optic disc and optic cup segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A hybrid framework integrating active contour and deep learning for optic disc and optic cup segmentation

Reference 20

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.319309Z

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Observation ebc98625-3b4f-4fa2-a471-3316cd356ddd · outbound

This paper cites A novel deep neural architecture for efficient and scalable multi-domain image classification.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A novel deep neural architecture for efficient and scalable multi-domain image classification

Reference 21

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Observation 6c021c63-45c0-4b4c-b65a-dc836f86aabb · outbound

This paper cites GLAC-UNet: global-local active contour loss with an efficient U-shaped architecture for multiclass medical image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function GLAC-UNet: global-local active contour loss with an efficient U-shaped architecture for multiclass medical image segmentation

Reference 22

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.339372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03c00511-f5fd-4fde-b730-30f5300f611a · outbound

This paper cites Context-driven active contour (CDAC): a novel medical image segmentation method based on active contour and contextual understanding.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Context-driven active contour (CDAC): a novel medical image segmentation method based on active contour and contextual understanding

Reference 23

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.261799Z

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Observation 253dea79-7c63-45a9-912b-f7000d76ca39 · outbound

This paper cites Topology-guaranteed image segmentation: enforcing connectivity, genus, and width constraints.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Topology-guaranteed image segmentation: enforcing connectivity, genus, and width constraints

Reference 24

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.281321Z

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Observation 38322617-6fda-4f88-a59b-6d2d3f36e0a1 · outbound

This paper cites CurvDrop: data-efficient learning for medical image segmentation via curvature-based sample selection.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function CurvDrop: data-efficient learning for medical image segmentation via curvature-based sample selection

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-15T05:00:59.309563Z

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Observation cb1dcb7c-7a1e-4e10-b8d8-b0f78d83156c · outbound

This paper cites Optimal approximations by piecewise smooth functions and associated variational problems.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Optimal approximations by piecewise smooth functions and associated variational problems

Reference 26

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This paper cites The Potts model with different piecewise constant representations and fast algorithms: a survey.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function The Potts model with different piecewise constant representations and fast algorithms: a survey

Reference 27

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Observation d926f82b-156e-4f50-8aa8-18a87441c621 · outbound

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Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Unresolved cited work

Reference 28

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Observation 560dab47-a15a-4389-8baa-f61a33e6b03c · outbound

This paper cites Oktay, J.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Oktay, J

Reference 29

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Observation ce2d4883-ee91-4fd1-a801-6b3bf319f443 · outbound

This paper cites Learning Euler's Elastica Model for Medical Image Segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Learning Euler's Elastica Model for Medical Image Segmentation

Reference 30

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Observation 5f634788-364b-4031-be48-dba8a690a0e5 · outbound

This paper cites Robust variational model based tailored UNet: leveraging edge detector and mean curvature for improved image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Robust variational model based tailored UNet: leveraging edge detector and mean curvature for improved image segmentation

Reference 31

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This paper cites Discriminative curvature regularization loss for boundary segmentation in microscopy cell images.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Discriminative curvature regularization loss for boundary segmentation in microscopy cell images

Reference 32

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Observation 14abeb5d-675c-40f3-a382-e3e913a0b935 · outbound

This paper cites EDU-Net: Retinal Pathological Fluid Segmentation in OCT Images with Multiscale Feature Fusion and Boundary Optimization.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function EDU-Net: Retinal Pathological Fluid Segmentation in OCT Images with Multiscale Feature Fusion and Boundary Optimization

Reference 33

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Observation 808ecb58-14d2-4b9d-aa08-bc3221381065 · outbound

This paper cites A median filter scheme for mean curvature flow.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function A median filter scheme for mean curvature flow

Reference 34

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.326695Z

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Observation 2a836747-72e2-4f52-867c-b0805d36a48d · outbound

This paper cites Active contour models driven by hyperbolic mean curvature flow for image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Active contour models driven by hyperbolic mean curvature flow for image segmentation

Reference 35

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Observation 1f25731e-6af9-4c51-83c0-0c84a4e9ba92 · outbound

This paper cites Variational and PDE-based static and video image segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Variational and PDE-based static and video image segmentation

Reference 36

Resolution: verified exact; doi, observed 2026-07-15T05:00:59.313296Z

Source-reported events for the cited work

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Observation 6041ce3e-f38c-4871-9e84-560e4ccfc0dd · outbound

This paper cites CHAOS challenge: combined (CT- MR) healthy abdominal organ segmentation.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function CHAOS challenge: combined (CT- MR) healthy abdominal organ segmentation

Reference 37

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Observation dbe3f4bc-8a42-41fc-9cf9-1baa95a0380b · outbound

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Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Unresolved cited work

Reference 38

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