Paper Citation Record · LEDGER

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.06966.

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

pith.paper-citation-record.v1
2507.06966 v1

Coverage vector

measured 30 of 30 reference resolution

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measured 30 of 30 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation dc245d6c-86aa-429f-9851-d8be299b4425 · outbound

This paper cites Clinical implementation of magnetic resonance imaging guided adaptive radiotherapy for localized prostate cancer.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Clinical implementation of magnetic resonance imaging guided adaptive radiotherapy for localized prostate cancer

Reference 1

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correction dated 2020-08-14. Source: crossref record 10.1016/j.phro.2020.07.008->10.1016/j.phro.2019.02.002:correction, observed 2026-07-11T03:15:46.465255+00:00. This notice travels one citation hop only.

Observation a179c06c-0aa6-4f78-9a1b-6d3d331d70c7 · outbound

This paper cites Magnetic Resonance Imaging–Guided vs Computed Tomography–Guided Stereotactic Body Radiotherapy for Prostate Cancer: The MIRAGE Randomized Clinical Trial.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Magnetic Resonance Imaging–Guided vs Computed Tomography–Guided Stereotactic Body Radiotherapy for Prostate Cancer: The MIRAGE Randomized Clinical Trial

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:55:42.008516Z

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Observation 1fa13da9-cbd3-45b8-8619-1a2bb7da893b · outbound

This paper cites Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.339465Z

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Observation 4befa8dc-98c1-4612-a115-0187b73b6745 · outbound

This paper cites End-to-end empirical validation of dose accumulation in MRI-guided adaptive radiotherapy for prostate cancer using an anthropomorphic deformable pelvis phantom.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy End-to-end empirical validation of dose accumulation in MRI-guided adaptive radiotherapy for prostate cancer using an anthropomorphic deformable pelvis phantom

Reference 4

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Observation 4a465989-542e-4a29-881a-b8b0b88f7b25 · outbound

This paper cites Validation of an MR-guided online adaptive radiotherapy (MRgoART) program: Deformation accuracy in a heterogeneous, deformable, anthropomorphic phantom.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Validation of an MR-guided online adaptive radiotherapy (MRgoART) program: Deformation accuracy in a heterogeneous, deformable, anthropomorphic phantom

Reference 5

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.316454Z

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Observation 9e515e7a-24b9-4391-8807-c52b6ef69293 · outbound

This paper cites A multi-institutional comparison of retrospective deformable dose accumulation for online adaptive magnetic resonance- guided radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy A multi-institutional comparison of retrospective deformable dose accumulation for online adaptive magnetic resonance- guided radiotherapy

Reference 6

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Observation 44262d59-a296-4a05-b0de-ae9c222db4ea · outbound

This paper cites an unresolved cited work.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Unresolved cited work

Reference 7

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Observation 720069bb-a119-4105-b63b-a8dd7a1f15d7 · outbound

This paper cites Dose accumulation of adapted treatment plans in MR-guided radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Dose accumulation of adapted treatment plans in MR-guided radiotherapy

Reference 8

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Observation 41b0938d-f6a0-43d8-bbc1-2a7d8fa80df3 · outbound

This paper cites Geometric and Dosimetric Validation of Deformable Image Registration for Prostate MR-guided Adaptive Radiotherapy 2025.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Geometric and Dosimetric Validation of Deformable Image Registration for Prostate MR-guided Adaptive Radiotherapy 2025

Reference 9

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.303766Z

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Observation 9ba8a9b6-ea3a-467c-ad56-4446f88f2d31 · outbound

This paper cites an unresolved cited work.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Unresolved cited work

Reference 10

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Observation 74cf9b6a-e0b1-4e4c-a1a2-f9b2be761758 · outbound

This paper cites Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT

Reference 11

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Observation 68b62d92-7041-4b3a-af8b-53aa9e46ec0e · outbound

This paper cites VoxelMorph: A Learning Framework for Deformable Medical Image Registration.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy VoxelMorph: A Learning Framework for Deformable Medical Image Registration

Reference 12

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Observation c1013102-7d1f-4099-a559-d72cec06267c · outbound

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

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy A deep learning framework for unsupervised affine and deformable image registration

Reference 13

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Observation 7fa83448-cc69-43c5-aa3a-f2d6c11daf3b · outbound

This paper cites Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy

Reference 14

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.206980Z

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Observation 831e85d2-701a-4ec5-a106-88fa4c90b6fc · outbound

This paper cites Training Data Independent Image Registration with Gans Using Transfer Learning and Segmentation Information.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Training Data Independent Image Registration with Gans Using Transfer Learning and Segmentation Information

Reference 15

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Observation 69099bad-a8f2-4b7e-b3f2-88b7f78c2fcb · outbound

This paper cites On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.195667Z

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Observation d6ddf377-5d42-4116-882e-580244c865c4 · outbound

This paper cites Integration of operator- validated contours in deformable image registration for dose accumulation in radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Integration of operator- validated contours in deformable image registration for dose accumulation in radiotherapy

Reference 17

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Observation 568f7e47-a35f-466b-868e-3439162db3f3 · outbound

This paper cites Anatomically-adaptive multi-modal image registration for image-guided external-beam radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Anatomically-adaptive multi-modal image registration for image-guided external-beam radiotherapy

Reference 18

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.183385Z

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Observation 5f7b8f57-c48d-4c6a-bd57-a149a13141aa · outbound

This paper cites A contour-guided deformable image registration algorithm for adaptive radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy A contour-guided deformable image registration algorithm for adaptive radiotherapy

Reference 19

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.171835Z

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Observation 8b2dcde3-8ce1-4af4-a0d1-2515784b0ac5 · outbound

This paper cites MuSIC: Multi- Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy MuSIC: Multi- Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks

Reference 20

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.160322Z

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Observation 31a08c89-849a-4c66-9d0e-a1b8fa68321a · outbound

This paper cites Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer

Reference 21

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Observation 2a41bc8e-6dd9-4d40-884f-9a5a4c7b4d5c · outbound

This paper cites Semi-weakly-supervised neural network training for medical image registration.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Semi-weakly-supervised neural network training for medical image registration

Reference 22

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Observation 2ad04926-3d11-43cd-bfc3-a7a9dd032f12 · outbound

This paper cites A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery

Reference 23

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:55:41.561464Z

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Observation 71ed11b1-dd78-4108-84f6-def7057e6e95 · outbound

This paper cites Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer

Reference 24

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.134748Z

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Observation ee38fe7e-ae1f-47ef-b2c8-a0b26bdeb20e · outbound

This paper cites SBRT focal dose intensification using an MR-Linac adaptive planning for intermediate-risk prostate cancer: An analysis of the dosimetric impact of intra-fractional organ changes.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy SBRT focal dose intensification using an MR-Linac adaptive planning for intermediate-risk prostate cancer: An analysis of the dosimetric impact of intra-fractional organ changes

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:55:41.490111Z

Source-reported events for the cited work

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Observation dde5e217-9ca6-425c-b07e-215d584abaae · outbound

This paper cites The ANTsX ecosystem for quantitative biological and medical imaging.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy The ANTsX ecosystem for quantitative biological and medical imaging

Reference 26

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Observation bf9f63d2-01dc-43db-877f-2f99e353a9a3 · outbound

This paper cites Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:41.051091Z

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Observation d0855ab5-903e-4190-b5a6-2d23c340ecfa · outbound

This paper cites EVolution: an edge-based variational method for non-rigid multi-modal image registration.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy EVolution: an edge-based variational method for non-rigid multi-modal image registration

Reference 28

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.107821Z

Source-reported events for the cited work

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Observation 15c438ce-d79b-4fc8-86e7-f97c7fac37df · outbound

This paper cites Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T18:55:41.095973Z

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 4441747a-929c-4d9e-8ce7-5d75ed7aecb0 · outbound

This paper cites Domain Adaptation for Medical Image Analysis: A Survey.

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy Domain Adaptation for Medical Image Analysis: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:41.061968Z

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Pith citing papers

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