Paper Citation Record · LEDGER

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation

As of 22 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2412.10946.

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

pith.paper-citation-record.v1
2412.10946 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

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measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:21:18.402889Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-12T04:21:21.705722Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2fca519e-ae3d-4d3d-bdba-3b014c70e883 · outbound

This paper cites Multiple sclerosis pathology,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Multiple sclerosis pathology,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.668757Z

Source-reported events for the cited work

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Observation e85c98a9-223d-4e1c-bada-0b23f16e3f1b · outbound

This paper cites Longitudinal multiple sclerosis lesion segmentation: Resource and challenge,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Longitudinal multiple sclerosis lesion segmentation: Resource and challenge,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.654411Z

Source-reported events for the cited work

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Observation b41e3ef5-47d4-497e-b429-f791190c2c6a · outbound

This paper cites Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.639435Z

Source-reported events for the cited work

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Observation 0244d56e-9f46-49df-a700-c21b19509ae8 · outbound

This paper cites MICCAI 2021 MSSEG-2 challenge quantitative results,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation MICCAI 2021 MSSEG-2 challenge quantitative results,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.621559Z

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Observation 81910e76-f5d1-45c9-bbcf-999830cd90e5 · outbound

This paper cites Disappearing brainstem mri lesions in multiple sclerosis (p3.353),.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Disappearing brainstem mri lesions in multiple sclerosis (p3.353),

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.605363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 731db169-5ada-4cfe-9934-d0869ba553a5 · outbound

This paper cites Atro- phied brain lesion volume: A new imaging biomarker in multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Atro- phied brain lesion volume: A new imaging biomarker in multiple sclerosis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.589525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 995b2532-6f5c-4233-abec-dae69127249c · outbound

This paper cites Multiple sclerosis: pathology of recurrent lesions,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Multiple sclerosis: pathology of recurrent lesions,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.573434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d77f1193-0c58-4754-a2a8-e801c7d7f272 · outbound

This paper cites Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.557772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f1aa32f7-c804-44cd-b200-704877023aa4 · outbound

This paper cites LesionMix: A lesion-level data augmentation method for medical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation LesionMix: A lesion-level data augmentation method for medical image segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.540869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e91e2e43-533e-405f-aacb-330ca07bd7b2 · outbound

This paper cites Temporally consistent probabilistic detection of new mul- tiple sclerosis lesions in brain MRI,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Temporally consistent probabilistic detection of new mul- tiple sclerosis lesions in brain MRI,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.524876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88a1adba-5088-4438-be7e-865632670585 · outbound

This paper cites Two time point MS lesion segmentation in brain MRI: An expectation- maximization framework,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Two time point MS lesion segmentation in brain MRI: An expectation- maximization framework,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.508691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 916e1c1f-66ab-4bd4-bef7-4ec9d9c2b746 · outbound

This paper cites Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.492941Z

Source-reported events for the cited work

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Observation 23531c99-f4b0-4f28-86d9-f948f71fc9f9 · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomed- ical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation nnU-Net: a self-configuring method for deep learning-based biomed- ical image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.478726Z

Source-reported events for the cited work

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Observation 75b0323d-afdf-471b-b8df-b045ce6321b5 · outbound

This paper cites New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.462358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d12e3d24-91ef-4ca8-90ca-415afe97dc71 · outbound

This paper cites CoactSeg: Learning from heterogeneous data for new multiple sclerosis lesion segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation CoactSeg: Learning from heterogeneous data for new multiple sclerosis lesion segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.447288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7e46a11d-4295-45cb-b2b4-f2821898fab7 · outbound

This paper cites CLIP-driven universal model for organ segmentation and tumor detection,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation CLIP-driven universal model for organ segmentation and tumor detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.431525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 09eb340a-bce4-4bb2-b14d-29607b316063 · outbound

This paper cites Marginal loss and exclusion loss for partially supervised multi-organ segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Marginal loss and exclusion loss for partially supervised multi-organ segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.412701Z

Source-reported events for the cited work

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Observation 0fc6da5f-944b-436a-b2c1-619ecbcb6319 · outbound

This paper cites Universeg: Universal medical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Universeg: Universal medical image segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.396166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b8d250e2-735a-44d1-a195-de20820a559a · outbound

This paper cites Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

Reference 19

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Source-reported events for the cited work

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Observation e6d7f35c-dbd7-4754-8e02-c8e04ba964dc · outbound

This paper cites Anatomical priors in convolutional networks for unsupervised biomedical segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Anatomical priors in convolutional networks for unsupervised biomedical segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.379796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8321b510-e260-4d4f-a5fd-fc21a4c50dd0 · outbound

This paper cites White matter MS-lesion segmentation using a geometric brain model,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation White matter MS-lesion segmentation using a geometric brain model,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.361591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ac7ff284-76e0-4cd6-8fed-dfa9617ae784 · outbound

This paper cites Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.345514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d2ddab2-64ab-4e13-a171-9d04cce2278d · outbound

This paper cites CarveMix: A simple data augmentation method for brain lesion segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation CarveMix: A simple data augmentation method for brain lesion segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.327201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7ca19e78-4fe4-458f-92d7-671ace2758a6 · outbound

This paper cites A structural causal model for MR images of multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation A structural causal model for MR images of multiple sclerosis,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.311286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82a220e3-c70c-4dd7-9880-e5a891f27468 · outbound

This paper cites Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.293883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86dad3f3-ebb7-4c32-b404-29a67a03a65a · outbound

This paper cites SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

Reference 26

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Source-reported events for the cited work

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Observation fd9f7760-2c75-4e41-86a0-267927ac8a9a · outbound

This paper cites SynthSeg: Segmentation of brain MRI scans of any contrast and resolu- tion without retraining,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation SynthSeg: Segmentation of brain MRI scans of any contrast and resolu- tion without retraining,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.278864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 84188be8-a4f2-49d4-a738-4ee6f629434c · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.262817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 233b3354-f19a-483b-9e0f-6785bc9330f4 · outbound

This paper cites Evaluation of the statistical detection of change algorithm for screening patients with MS with new lesion activity on longitudinal brain MRI,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Evaluation of the statistical detection of change algorithm for screening patients with MS with new lesion activity on longitudinal brain MRI,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.246316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7422d7a9-df9a-4ba4-ae4f-2e7533afcb0e · outbound

This paper cites Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.228104Z

Source-reported events for the cited work

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Observation 7d553972-b004-4f05-b91c-b0dc9db1e9a6 · outbound

This paper cites Imaging biomarkers in multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Imaging biomarkers in multiple sclerosis,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.210201Z

Source-reported events for the cited work

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Observation feb94608-2cc2-4cad-87f8-8a44767896ed · outbound

This paper cites Predictive mri biomarkers in ms—a critical review,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Predictive mri biomarkers in ms—a critical review,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.191611Z

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Observation 09c9bfba-3f18-48d6-9c85-e921967a5e2f · outbound

This paper cites Slowly eroding lesions in multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Slowly eroding lesions in multiple sclerosis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.174870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b08a1e2c-b7cb-4b66-91bd-fbe4b3f92556 · outbound

This paper cites Atrophied brain T2 lesion volume at MRI is associated with disability progression and conversion to secondary progressive multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Atrophied brain T2 lesion volume at MRI is associated with disability progression and conversion to secondary progressive multiple sclerosis,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.159790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 63fcf6bb-baf0-40bf-bba1-5fb80e3f2d6b · outbound

This paper cites Correlations between monthly enhanced mri lesion rate and changes in t2 lesion volume in multiple sclerosis,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Correlations between monthly enhanced mri lesion rate and changes in t2 lesion volume in multiple sclerosis,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.144967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 91222d34-fcaa-4106-b6da-0e1513a354e3 · outbound

This paper cites Curriculum learning,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Curriculum learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.128797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3acaba10-974f-4e95-9381-5361fd5d4126 · outbound

This paper cites An image inpainting technique based on the fast marching method,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation An image inpainting technique based on the fast marching method,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.110840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 73b6f13c-7b7b-4036-bed9-76e02c18d286 · outbound

This paper cites An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.094777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0f1a9c1a-188c-40df-8185-696f9df6b089 · outbound

This paper cites Block-matching strategies for rigid registration of multimodal medical images,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Block-matching strategies for rigid registration of multimodal medical images,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.078878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e165b860-7c7e-4b76-ac3d-df65cceb2d79 · outbound

This paper cites volbrain: An online mri brain volumetry system,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation volbrain: An online mri brain volumetry system,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.062248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d6b4f21f-058a-49e8-b960-d21044c07678 · outbound

This paper cites N4itk: Improved n3 bias correction,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation N4itk: Improved n3 bias correction,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.046774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d895d68-1789-4442-b6f8-abee72109fd6 · outbound

This paper cites Multi-Atlas Skull-Stripping,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Multi-Atlas Skull-Stripping,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.030931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2b543855-f12a-4857-81f8-8c5960771966 · outbound

This paper cites Itk-snap: An interactive tool for semi-automatic segmentation of multi- modality biomedical images,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Itk-snap: An interactive tool for semi-automatic segmentation of multi- modality biomedical images,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:44.013888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cf26b604-3395-45a4-bfc4-cb70b45b0c4e · outbound

This paper cites Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.997179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8afd9320-e5c2-4ee6-9d61-d67f5a524d5f · outbound

This paper cites Longitudinal detection of new ms lesions using deep learning,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Longitudinal detection of new ms lesions using deep learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.980634Z

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Observation 39b7bb5f-4a54-4055-804c-ece1fe992c3a · outbound

This paper cites nnFormer: Volumetric medical image segmentation via a 3D transformer,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation nnFormer: Volumetric medical image segmentation via a 3D transformer,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.962349Z

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Observation 274babf5-3409-48b8-aafd-51dcc9b4ff53 · outbound

This paper cites UNETR: Transformers for 3D medical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation UNETR: Transformers for 3D medical image segmentation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.946696Z

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Observation 0734bbe7-9ead-49a7-a1b3-bbbfc890cfce · outbound

This paper cites Segmentation of new MS lesions with tiramisu and 2.5D stacked slices,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Segmentation of new MS lesions with tiramisu and 2.5D stacked slices,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.929864Z

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Observation 94b6635f-e415-4ca6-99a4-851bc3139e6e · outbound

This paper cites TransBTS: Multimodal brain tumor segmentation using transformer,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation TransBTS: Multimodal brain tumor segmentation using transformer,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.912759Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 068faa7a-e54d-4eb1-abe7-64a80736ee45 · outbound

This paper cites Transunet: Transformers make strong encoders for medical image segmentation,.

SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation Transunet: Transformers make strong encoders for medical image segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:31:43.895584Z

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Pith citing papers

Observation 1178d59b-f19b-402a-b310-425489840dce · inbound

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models cites this paper.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:21:21.708632Z

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