PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation | Paper Citation Record

Observation 559a32dc-db52-4df5-8d65-6ad579229f44 · outbound

This paper cites ArtificialIntelligenceinVascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation ArtificialIntelligenceinVascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:22.102193Z

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This paper cites Pattern Recognition Letters 139, 118–127.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Pattern Recognition Letters 139, 118–127

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:22.088716Z

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Observation df70fc6b-aa55-487b-bb05-6161379ac114 · outbound

This paper cites Engineering Science and Technology, an International Journal 24, 271–283.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Engineering Science and Technology, an International Journal 24, 271–283

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:22.076007Z

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Observation e80a8a82-3a6e-4348-a770-07cd89c0ae9b · outbound

This paper cites Variations in coronary artery diameter: a retrospective observational study in Indian population.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Variations in coronary artery diameter: a retrospective observational study in Indian population

Reference 4

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Observation fff19ab3-a911-413f-b929-9a5dbd09e5ec · outbound

This paper cites TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:22.061966Z

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This paper cites Generalized overlap measures for evaluation and validation in medical image analysis.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Generalized overlap measures for evaluation and validation in medical image analysis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:21.958572Z

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, in: 9th International Conference on Learning Representations, ICLR.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation An image is worth 16x16 words: Transformers for image recognition at scale, in: 9th International Conference on Learning Representations, ICLR

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:21.873938Z

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Observation 11a32d44-4272-4879-88a9-66dbde1e7c5d · outbound

This paper cites Automating vessel segmentation in the heart and brain: A trend to develop multi-modality and label-efficient deep learning techniques.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Automating vessel segmentation in the heart and brain: A trend to develop multi-modality and label-efficient deep learning techniques

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:21.588596Z

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Observation a82ddc21-e2d1-46a2-adb5-eb9222a767ef · outbound

This paper cites Multiscale vessel enhancement filtering, in: Medical Image Comput- ing and Computer-Assisted Intervention, Springer.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Multiscale vessel enhancement filtering, in: Medical Image Comput- ing and Computer-Assisted Intervention, Springer

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:21.389905Z

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Observation 5cabf039-de2e-4ebe-bdaf-dfa1b09614c7 · outbound

This paper cites tUbe net: a generalisable deep learning tool for 3D vessel segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation tUbe net: a generalisable deep learning tool for 3D vessel segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:21.130575Z

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This paper cites an unresolved cited work.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Unresolved cited work

Reference 11

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Observation 9ef3b357-7744-4d72-9473-99cc86ff849f · outbound

This paper cites Boundary attention assisted dynamic graph convolution for retinal vascular segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Boundary attention assisted dynamic graph convolution for retinal vascular segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:20.830712Z

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This paper cites Dual-Branch- UNet:Adual-branchconvolutionalneuralnetworkformedicalimage segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Dual-Branch- UNet:Adual-branchconvolutionalneuralnetworkformedicalimage segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:20.694006Z

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This paper cites Fives: A fundus image dataset for artificial Intelligence based vessel segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Fives: A fundus image dataset for artificial Intelligence based vessel segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:20.567891Z

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Observation a9b12984-8615-4d74-85a3-17b66aa12949 · outbound

This paper cites Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

Reference 15

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This paper cites Pattern Recognition 165, 111544.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Pattern Recognition 165, 111544

Reference 16

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This paper cites A review of vessel extraction techniques and algorithms.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation A review of vessel extraction techniques and algorithms

Reference 17

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PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Unresolved cited work

Reference 18

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This paper cites U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:19.715542Z

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This paper cites Topology-jointCurvilinear Segmentation Network using Confidence-based Bezier Topological Representation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Topology-jointCurvilinear Segmentation Network using Confidence-based Bezier Topological Representation

Reference 20

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Observation d101645e-979a-4bc7-95ef-5c980782660a · outbound

This paper cites Adaptivefeature fusion cascade Transformer retinal vessel segmentation algorithm.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Adaptivefeature fusion cascade Transformer retinal vessel segmentation algorithm

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:19.393074Z

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Observation cf4f3654-ec6a-48ab-a2d7-e01b2f2ae189 · outbound

This paper cites Transformer and con- volutional based dual branch network for retinal vessel segmentation in OCTA images.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Transformer and con- volutional based dual branch network for retinal vessel segmentation in OCTA images

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:19.225068Z

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This paper cites A U-Net deep learning framework for high performance vessel seg- mentation in patients with cerebrovascular disease.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation A U-Net deep learning framework for high performance vessel seg- mentation in patients with cerebrovascular disease

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:19.024452Z

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This paper cites Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 24

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This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 25

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This paper cites CoANet:Connectivity attention network for road extraction from satellite imagery.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation CoANet:Connectivity attention network for road extraction from satellite imagery

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:18.561565Z

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This paper cites Journal of the American College of Cardiology 82, 2350–2473.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Journal of the American College of Cardiology 82, 2350–2473

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:18.260999Z

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This paper cites CS2-Net:Deeplearning segmentation of curvilinear structures in medical imaging.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation CS2-Net:Deeplearning segmentation of curvilinear structures in medical imaging

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:17.932098Z

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Observation b2fcb45e-b8cb-42d8-941e-8a8389b26073 · outbound

This paper cites Usingdeeplearningfor an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Usingdeeplearningfor an automatic detection and classification of the vascular bifurcations along the Circle of Willis

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:17.560672Z

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This paper cites EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:17.199927Z

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Observation 2dcad4dc-bb50-431a-9d3e-bbb0502736e8 · outbound

This paper cites Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:16.859475Z

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PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Unresolved cited work

Reference 32

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PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:05:16.453535Z

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This paper cites Imagesimilarityandtissueoverlapsassurrogates for image registration accuracy: widely used but unreliable.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Imagesimilarityandtissueoverlapsassurrogates for image registration accuracy: widely used but unreliable

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:16.233386Z

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Observation 159dc2b1-b5bb-4a25-8f8a-072e94ba7c11 · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, Springer.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation U-Net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, Springer

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:16.063033Z

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Observation 7d5aba0c-33af-487f-9113-ecbcf339bb87 · outbound

This paper cites Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:05:13.813089Z

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Observation 9cffc27a-6701-4b68-9a01-ff290dd445a5 · outbound

This paper cites FreeCOS: Self- supervisedlearningfromfractalsandunlabeledimagesforcurvilinear object segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation FreeCOS: Self- supervisedlearningfromfractalsandunlabeledimagesforcurvilinear object segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:15.886893Z

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This paper cites Affinityfeaturestrengtheningforaccurate,completeandrobustvessel segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Affinityfeaturestrengtheningforaccurate,completeandrobustvessel segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:15.721668Z

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Observation c37722d8-d96f-4e2c-9847-811d5fc07a4b · outbound

This paper cites clDice-a novel topology-preservinglossfunctionfortubularstructuresegmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation clDice-a novel topology-preservinglossfunctionfortubularstructuresegmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:15.452273Z

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Observation cb6925b2-7f66-47f5-872f-ba0c955cc9a3 · outbound

This paper cites Multi-Level Medical Image Segmentation Network Based on Multi- Scale and Context Information Fusion Strategy.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Multi-Level Medical Image Segmentation Network Based on Multi- Scale and Context Information Fusion Strategy

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:15.232917Z

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Observation 239509d5-0b56-453b-af20-97da1e1ff235 · outbound

This paper cites an unresolved cited work.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:05:15.054243Z

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Observation 9a5ef24e-a52b-4c43-b24d-b90bfd5c1b3b · outbound

This paper cites A three-stage deep learning model for accurate retinal vessel segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation A three-stage deep learning model for accurate retinal vessel segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.734574Z

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Observation bed66b9a-246a-4a5d-a483-91c7be04651e · outbound

This paper cites Jointsegment-levelandpixel- wiselossesfordeeplearningbasedretinalvesselsegmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Jointsegment-levelandpixel- wiselossesfordeeplearningbasedretinalvesselsegmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.594426Z

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Observation cd2f641f-e4ce-4aba-8c34-f1ef8a7902b1 · outbound

This paper cites An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.424397Z

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Observation a6823d77-5fe0-458a-b381-096d06d344b9 · outbound

This paper cites Progressive deep segmentation of coronary arteryviahierarchicaltopologylearning,in:InternationalConference on Medical Image Computing and Computer-Assisted Intervention, Springer.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Progressive deep segmentation of coronary arteryviahierarchicaltopologylearning,in:InternationalConference on Medical Image Computing and Computer-Assisted Intervention, Springer

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.221141Z

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Observation 19225ee9-0ef1-47f6-a263-ef85c2eb1e99 · outbound

This paper cites Road Extraction by Deep Residual U-Net.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Road Extraction by Deep Residual U-Net

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.058864Z

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Observation cab31925-825a-4216-98bb-5b25c52a9021 · outbound

This paper cites AnestedU-shapenetworkwith multi-scale upsample attention for robust retinal vascular segmenta- tion.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation AnestedU-shapenetworkwith multi-scale upsample attention for robust retinal vascular segmenta- tion

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:13.949255Z

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Observation 1c67ae26-7890-4287-bca0-5eb6e9c14f59 · outbound

This paper cites Nature Methods 18, 203–211.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Nature Methods 18, 203–211

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:20.978307Z

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Observation 11163a87-8dd8-4beb-a03e-e7b728f76220 · outbound

This paper cites Medical Image Analysis 102, 103547.

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation Medical Image Analysis 102, 103547

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:05:14.901885Z

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