Paper Citation Record · LEDGER

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.05104.

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

pith.paper-citation-record.v1
1908.05104 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:27:10.611348Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

41 of 41 outbound references displayed

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verified fuzzy40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c7f1dfe-7e00-4d4c-86c0-4dc88df97849 · outbound

This paper cites Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.702857Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ed83b6a-39ac-43e9-95c2-1f4db4eb4aae · outbound

This paper cites Liver tumor volume estimation by semi-automatic segmentation method[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Liver tumor volume estimation by semi-automatic segmentation method[C]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.667864Z

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Observation 7d41b115-f1c7-4bc7-bc51-eea92870fafa · outbound

This paper cites Acute ischemic stroke[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Acute ischemic stroke[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.645691Z

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Observation 56160aab-7ef1-41ad-93f5-7ef997a2830c · outbound

This paper cites Cost of stroke in the United King- dom[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Cost of stroke in the United King- dom[J]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.608788Z

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Observation 5d483924-792d-4730-8a82-9e6aa038a646 · outbound

This paper cites Interrater agreement for ﬁnal infarct MRI lesion delineation[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Interrater agreement for ﬁnal infarct MRI lesion delineation[J]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.586279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d0104f1-9f21-4c61-bee3-d85a44586c89 · outbound

This paper cites Measurement of in- farct volume in stroke patients using adaptive segmentation of diffusion weighted MR images[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Measurement of in- farct volume in stroke patients using adaptive segmentation of diffusion weighted MR images[C]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.561064Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5250beb9-d27d-41c3-883d-6b08cd44785b · outbound

This paper cites A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.542942Z

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Observation b95c66ce-2a98-4767-994b-9cfe8b148d45 · outbound

This paper cites Ishemic Stroke Lesion Segmentation by Analyzing MRI Images Using Dilated and Transposed Convolutions in Con- volutional Neural Networks[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Ishemic Stroke Lesion Segmentation by Analyzing MRI Images Using Dilated and Transposed Convolutions in Con- volutional Neural Networks[C]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.525237Z

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Observation ce067bf7-1c28-4f2b-9eac-9fab43c30fd7 · outbound

This paper cites Towards clinical diagnosis: Automated stroke lesion segmentation on multi-spectral MR image using con- volutional neural network[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Towards clinical diagnosis: Automated stroke lesion segmentation on multi-spectral MR image using con- volutional neural network[J]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.500466Z

Source-reported events for the cited work

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Observation af20b30b-5d5b-4842-a1a8-1f8c1b7e84b3 · outbound

This paper cites Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.476366Z

Source-reported events for the cited work

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Observation a23a4cf7-7b1e-435a-a22b-6350e82fa787 · outbound

This paper cites Stroke lesion detection using convolutional neural networks[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Stroke lesion detection using convolutional neural networks[C]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.453664Z

Source-reported events for the cited work

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Observation bd0c848a-d8b2-43df-9c01-b553daff130c · outbound

This paper cites Optimizing U- Net to Segment Left Ventricle from Magnetic Resonance Imag- ing[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Optimizing U- Net to Segment Left Ventricle from Magnetic Resonance Imag- ing[C]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.426397Z

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Observation 4c1fefaa-664a-487c-8cc2-7b4e63ef0b86 · outbound

This paper cites Classiﬁcation of Atrial Fib- rillation with Pre-Trained Convolutional Neural Network Mod- els[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Classiﬁcation of Atrial Fib- rillation with Pre-Trained Convolutional Neural Network Mod- els[C]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.262452Z

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Observation b1ce4616-8c49-451c-9333-350c493a0c88 · outbound

This paper cites Drinet for medical image seg- mentation[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Drinet for medical image seg- mentation[J]

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.243665Z

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Observation 47809710-8d8b-46ef-9d7c-37ddefe5fe7c · outbound

This paper cites Automatic real-time CNN- based neonatal brain ventricles segmentation[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Automatic real-time CNN- based neonatal brain ventricles segmentation[C]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.220632Z

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Observation da29bfb2-ef89-4e4d-ac65-daf8a6b46896 · outbound

This paper cites 3D fully convolutional networks for co-segmentation of tumors on PET-CT images[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation 3D fully convolutional networks for co-segmentation of tumors on PET-CT images[C]

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.199626Z

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Observation 51feaefa-5c8f-4727-ac9f-2d3ee0f593b0 · outbound

This paper cites Image segmentation of liver CT based on fully convolutional network[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Image segmentation of liver CT based on fully convolutional network[C]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.177908Z

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Observation d4fbdddc-28d6-49ad-b3fa-d0f25b645f3d · outbound

This paper cites Querying Representative and Informative Super-pixels for Filament Segmentation in Bioim- ages[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Querying Representative and Informative Super-pixels for Filament Segmentation in Bioim- ages[J]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.151691Z

Source-reported events for the cited work

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Observation dc6ee409-77a8-4333-8b0e-18cb1e4e72c6 · outbound

This paper cites Multivariate dy- namic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Multivariate dy- namic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization[J]

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.133009Z

Source-reported events for the cited work

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Observation b6cab1ae-9f26-45ca-a2f8-8c3fa2fba164 · outbound

This paper cites Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography.

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.111654Z

Source-reported events for the cited work

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Observation a8e3c926-e736-450c-978c-76917c130b79 · outbound

This paper cites An active learn- ing approach for stroke lesion segmentation on multimodal MRI data[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation An active learn- ing approach for stroke lesion segmentation on multimodal MRI data[J]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.090377Z

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Observation f6fe058b-3a17-469d-931d-6a703d073d61 · outbound

This paper cites An efﬁcient automated methodol- ogy for detecting and segmenting the ischemic stroke in brain MRI images[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation An efﬁcient automated methodol- ogy for detecting and segmenting the ischemic stroke in brain MRI images[J]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.068273Z

Source-reported events for the cited work

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Observation b3a980e4-7050-42b5-abe9-4a131604551f · outbound

This paper cites Fully automatic acute ischemic le- sion segmentation in DWI using convolutional neural networks[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Fully automatic acute ischemic le- sion segmentation in DWI using convolutional neural networks[J]

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.042856Z

Source-reported events for the cited work

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Observation 0f051b3e-2507-4d3d-8430-43838b620f07 · outbound

This paper cites Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network[J]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:11.022284Z

Source-reported events for the cited work

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Observation d9289efb-59cd-42e1-88e7-ad8bed7f612d · outbound

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

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Efﬁcient multi- scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.996189Z

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Observation 3b9e37e9-4d91-46ec-80b5-bd5d3673c0c9 · outbound

This paper cites A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection[J]

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.975611Z

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Observation 73f130d7-58e1-4260-9739-b39760487ca7 · outbound

This paper cites Deep convolutional neu- ral networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Deep convolutional neu- ral networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.945394Z

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Observation 79d77176-51a1-4dd0-b734-3cfbad620f69 · outbound

This paper cites 3D convolutional neural networks for human action recognition[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation 3D convolutional neural networks for human action recognition[J]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.925155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a0fc45d3-5d72-4f3c-afea-c39978b09204 · outbound

This paper cites H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.898120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ff9bd1d-7103-465f-b9d0-b1de9a4318d8 · outbound

This paper cites Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy[J]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.875862Z

Source-reported events for the cited work

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Observation 6b3ca3e6-4a9e-48bc-89f2-9b33013604f6 · outbound

This paper cites Focal loss for dense object detection[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Focal loss for dense object detection[C]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.852686Z

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Observation 8954ef0e-f7a1-4685-8054-1006acd4735d · outbound

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

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.832934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59c5edf2-ce30-4b36-abcf-77b8d293ebf6 · outbound

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

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation U-net: Convolutional networks for biomedical image segmentation[C]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.811219Z

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Observation aed8d313-9210-4be0-b3f8-9f18fffaece6 · outbound

This paper cites Squeeze-and-excitation networks[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Squeeze-and-excitation networks[C]

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.790346Z

Source-reported events for the cited work

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Observation 1565238f-6c97-45c7-8627-8a606861065e · outbound

This paper cites Delving deep into rectiﬁers: Sur- passing human-level performance on imagenet classiﬁcation[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Delving deep into rectiﬁers: Sur- passing human-level performance on imagenet classiﬁcation[C]

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.772043Z

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Observation 75495b2c-bcf5-40f0-bc29-9865e7f04030 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Adam: A Method for Stochastic Optimization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-14T13:27:10.577873Z

Source-reported events for the cited work

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Observation 95215e7b-450c-4b08-b7ca-61911f64654f · outbound

This paper cites A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data[J]

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.753387Z

Source-reported events for the cited work

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Observation b5e791bc-2dc5-4d7a-8a72-93f5cbfc3103 · outbound

This paper cites Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation[J].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation[J]

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.736381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77e4aa6b-fc80-4119-8b3c-30e368fb4b7d · outbound

This paper cites Pyramid scene parsing network[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Pyramid scene parsing network[C]

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.718771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b9843152-2287-4081-a75d-2676274871ab · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation[C]

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.700952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 16ba774e-8714-41bd-809a-e112a7fe0cd3 · outbound

This paper cites Beyond the pixel- wise loss for topology-aware delineation[C].

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Beyond the pixel- wise loss for topology-aware delineation[C]

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:27:10.682069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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