Paper Citation Record · LEDGER

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:1908.04466.

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

pith.paper-citation-record.v1
1908.04466 v4

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:46:33.395308Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6b141f1-d869-4c53-b706-a1b824b639c3 · outbound

This paper cites The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience

Reference 1

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Observation 0b5b766a-b311-4f08-9674-669ecb81e7b4 · outbound

This paper cites Deep learning for brain mri segmentation: State of the art and future directions.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Deep learning for brain mri segmentation: State of the art and future directions

Reference 2

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Observation 2f91bdd6-02b6-41a2-8f5e-539c4e67129e · outbound

This paper cites Artaechevarria, A.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Artaechevarria, A

Reference 3

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This paper cites A fast diffeomorphic image registration algorithm.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation A fast diffeomorphic image registration algorithm

Reference 4

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Observation e0d6ef63-20d3-4f89-8ee2-f40fee1479e6 · outbound

This paper cites Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Symmetric diffeo- morphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

Reference 5

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Observation 0d09b788-338a-436d-81ed-3b4928fdf163 · outbound

This paper cites Multiresolution elastic matching.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Multiresolution elastic matching

Reference 6

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Observation 5c152a77-1ea3-4921-a473-7d7409d26bf6 · outbound

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Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Unresolved cited work

Reference 7

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Observation ea8c738a-81d5-4bfd-82a3-53077f8e9243 · outbound

This paper cites Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Computing large deforma- tion metric mappings via geodesic ﬂows of diffeomorphisms

Reference 8

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Observation 7a50d206-d422-4356-9361-7805bc7c9acf · outbound

This paper cites Semi-Supervised and Task-Driven Data Augmentation.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Semi-Supervised and Task-Driven Data Augmentation

Reference 9

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Observation 4d494ae1-3936-4728-9d9c-5face4c1f9d6 · outbound

This paper cites Harvard aging brain study: dataset and accessibility.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Harvard aging brain study: dataset and accessibility

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:46:33.814113Z

Source-reported events for the cited work

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Observation a1eb2941-6bb9-48d6-aa3d-dfa02e87c1b0 · outbound

This paper cites Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces

Reference 11

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Observation d010f204-ed9c-4189-af09-20203ff3228b · outbound

This paper cites Patch-based discrete registration of clinical brain images.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Patch-based discrete registration of clinical brain images

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:46:33.790925Z

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Observation b118765c-79db-4c95-8e39-c795d258260f · outbound

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

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Anatomical priors in convolutional networks for unsupervised biomedical segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:46:33.779726Z

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Observation a0ac5e35-b210-4776-9713-4d5abab74d38 · outbound

This paper cites A deep learning framework for unsupervised afﬁne and deformable image registration.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation A deep learning framework for unsupervised afﬁne and deformable image registration

Reference 14

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Observation 9f9c1a04-c1c6-4929-b2e7-4c905bedc2a5 · outbound

This paper cites The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Reference 15

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Observation 11dc500e-5e53-4d7f-a8bb-a05aa989814e · outbound

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Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Unresolved cited work

Reference 16

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Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Unresolved cited work

Reference 17

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Observation bce0fc5f-f32b-4586-967f-9196c0f1cac6 · outbound

This paper cites The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia

Reference 18

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Observation 06ccc98d-8b76-4ebe-9cfe-b9913df583bb · outbound

This paper cites Automatic brain tumor detection and segmentation using u-net based fully convolutional networks.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Automatic brain tumor detection and segmentation using u-net based fully convolutional networks

Reference 19

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Observation c614e5c2-24d4-4e67-b865-120db18e0bdd · outbound

This paper cites Brain genomics superstruct project initial data release with structural, functional, and behavioral measures.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Brain genomics superstruct project initial data release with structural, functional, and behavioral measures

Reference 20

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Observation b9754df5-5884-4232-b8a9-005ef9566a7c · outbound

This paper cites Weakly-supervised convolutional neural networks for multimodal image registration.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Weakly-supervised convolutional neural networks for multimodal image registration

Reference 21

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Observation 12f6840d-23b8-4e7b-b2b6-94a0b7746e99 · outbound

This paper cites Differential data augmenta- tion techniques for medical imaging classiﬁcation tasks.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Differential data augmenta- tion techniques for medical imaging classiﬁcation tasks

Reference 22

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This paper cites Multi-atlas segmentation of biomedical images: a survey.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Multi-atlas segmentation of biomedical images: a survey

Reference 23

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Observation 44c5d9dd-3463-4a64-8fb0-dae530a655ed · outbound

This paper cites Deep multi-class segmentation without ground-truth labels.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Deep multi-class segmentation without ground-truth labels

Reference 24

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Reference 25

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Observation 5bef9eaa-8126-4210-a113-0adddeeb26f0 · outbound

This paper cites CNN-based Segmentation of Medical Imaging Data.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation CNN-based Segmentation of Medical Imaging Data

Reference 26

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This paper cites Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration

Reference 27

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Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Unresolved cited work

Reference 28

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Observation 9ea3f970-6619-4847-aec6-30a3592d68da · outbound

This paper cites Learn- ing a probabilistic model for diffeomorphic registration.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Learn- ing a probabilistic model for diffeomorphic registration

Reference 29

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This paper cites Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults

Reference 30

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Observation 5ea40f58-0c04-443a-a947-8314b0f3d3d4 · outbound

This paper cites The parkinson progression marker initiative (ppmi).

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation The parkinson progression marker initiative (ppmi)

Reference 31

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

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 32

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This paper cites Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (adni).

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (adni)

Reference 33

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This paper cites Pereira, A.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Pereira, A

Reference 34

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This paper cites U-net: Convolutional networks for biomedical image segmentation.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 35

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Observation 32ab0c64-4cb2-46f7-92e4-9c5ded19cbb5 · outbound

This paper cites A generative model for image segmenta- tion based on label fusion.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation A generative model for image segmenta- tion based on label fusion

Reference 36

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Observation 328b0660-301d-4993-88dd-5a6b4b1a7e64 · outbound

This paper cites Multi-atlas segmentation with joint label fusion.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Multi-atlas segmentation with joint label fusion

Reference 37

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This paper cites Quicksilver: Fast predictive image registration–a deep learning approach.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Quicksilver: Fast predictive image registration–a deep learning approach

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:46:33.480816Z

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Observation 5d011ec3-894d-4673-af83-9310a5489524 · outbound

This paper cites Data augmentation using learned transforms for one-shot medical image segmentation.

Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation Data augmentation using learned transforms for one-shot medical image segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:46:33.467309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.