Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation | Paper Citation Record

Observation a18afcc0-1c60-4db6-81b8-c2367bffe17a · outbound

This paper cites MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT

Reference 1

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This paper cites UK bi obank: an open access resource for identifying the causes of a wide r ange of complex diseases of middle and old age,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation UK bi obank: an open access resource for identifying the causes of a wide r ange of complex diseases of middle and old age,

Reference 2

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Observation e84c59b6-e4c3-45bb-bf99-af1e46d8b92a · outbound

This paper cites TotalSegme ntator: Robust segmentation of 104 anatomic structures in CT images ,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation TotalSegme ntator: Robust segmentation of 104 anatomic structures in CT images ,

Reference 3

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Observation 388941b5-459a-4947-8d90-62c3def003d1 · outbound

This paper cites Dataset with segmentations of 104 importan t anatomical structures in 1204 CT images,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Dataset with segmentations of 104 importan t anatomical structures in 1204 CT images,

Reference 4

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Observation fd4f2a41-20cc-422d-aef1-df0661a79008 · outbound

This paper cites MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI

Reference 5

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Observation 24c81180-31c9-410a-816b-38728e031329 · outbound

This paper cites TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

Reference 6

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This paper cites N a- tional cancer institute imaging data commons: Toward trans parency, reproducibility, and scalability in imaging artiﬁcial int elligence,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation N a- tional cancer institute imaging data commons: Toward trans parency, reproducibility, and scalability in imaging artiﬁcial int elligence,

Reference 7

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Observation 68154771-2c8a-4b76-b9c5-7c6b27367645 · outbound

This paper cites Auto mated segmentation of tissues using CT and MRI: a systematic revie w,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Auto mated segmentation of tissues using CT and MRI: a systematic revie w,

Reference 8

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This paper cites Pancreas volume in health and disease: a systematic review and meta- analysis,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Pancreas volume in health and disease: a systematic review and meta- analysis,

Reference 9

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This paper cites Assessment of kidney volumes from mri: acquisit ion and segmentation techniques,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Assessment of kidney volumes from mri: acquisit ion and segmentation techniques,

Reference 10

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This paper cites The value of m agnetic resonance imaging for radiotherapy planning,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation The value of m agnetic resonance imaging for radiotherapy planning,

Reference 11

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Observation 1c67a96f-f76d-427b-b7b7-f697e088a746 · outbound

This paper cites Integrated MRI-guided radiotherapy—opportunities and c hallenges,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Integrated MRI-guided radiotherapy—opportunities and c hallenges,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.679822Z

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Observation b6538e5a-a8ab-4a6e-92e9-3f1ab5eec6ff · outbound

This paper cites Radiomics: images ar e more than pictures, they are data,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Radiomics: images ar e more than pictures, they are data,

Reference 13

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Observation cdd01f44-3b3e-4f36-8c65-c6975e541fd7 · outbound

This paper cites CT and MRI of abdominal can cers: current trends and perspectives in the era of radiomics and a rtiﬁcial 10 IEEE TRANSACTIONS ON MEDICAL IMAGING intelligence,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation CT and MRI of abdominal can cers: current trends and perspectives in the era of radiomics and a rtiﬁcial 10 IEEE TRANSACTIONS ON MEDICAL IMAGING intelligence,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.658605Z

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This paper cites Recommendations for MRI-based contouring of gross tumor v olume and organs at risk for radiation therapy of pancreatic cance r,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Recommendations for MRI-based contouring of gross tumor v olume and organs at risk for radiation therapy of pancreatic cance r,

Reference 15

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This paper cites vOARiability: Interobserver and intermodality variabil ity analysis in oar contouring from head and neck CT and MR images,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation vOARiability: Interobserver and intermodality variabil ity analysis in oar contouring from head and neck CT and MR images,

Reference 16

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This paper cites U-Net: Convolutio nal net- works for biomedical image segmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation U-Net: Convolutio nal net- works for biomedical image segmentation,

Reference 17

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This paper cites A learning strategy for contrast-agnostic mri se gmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation A learning strategy for contrast-agnostic mri se gmentation,

Reference 18

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Observation 4f5cb893-91c1-4788-be1d-8f98a352ab61 · outbound

This paper cites A survey on transfer learning,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation A survey on transfer learning,

Reference 19

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This paper cites Fully automated multiorgan segmentation in abdom- inal magnetic resonance imaging with deep neural networks,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Fully automated multiorgan segmentation in abdom- inal magnetic resonance imaging with deep neural networks,

Reference 20

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This paper cites Deep learning-bas ed auto- mated abdominal organ segmentation in the uk biobank and ger man national cohort magnetic resonance imaging studies,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Deep learning-bas ed auto- mated abdominal organ segmentation in the uk biobank and ger man national cohort magnetic resonance imaging studies,

Reference 21

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Observation 5843db3f-fd36-4bd1-96d5-417f210a5229 · outbound

This paper cites Abdomennet: deep neural network for abdomin al organ segmentation in epidemiologic imaging studies,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Abdomennet: deep neural network for abdomin al organ segmentation in epidemiologic imaging studies,

Reference 22

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Observation e717c616-52d7-478a-876e-79c4c9baba95 · outbound

This paper cites Deep learning auto-segmentation on multi- sequence magnetic resonance images for upper abdominal org ans,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Deep learning auto-segmentation on multi- sequence magnetic resonance images for upper abdominal org ans,

Reference 23

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Observation 646d0d39-33ae-4346-9fec-b6cc28a2397c · outbound

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

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation nnU-Net: a self-conﬁguring method for deep learning-based biomedic al image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.544910Z

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Observation 753a3ac5-c3ca-4714-824a-9d051ff44064 · outbound

This paper cites The german national cohort: aims, st udy design and organization,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation The german national cohort: aims, st udy design and organization,

Reference 25

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Observation b3342518-97c3-4742-8e4d-6d70f5d10342 · outbound

This paper cites Lymph node detection in t2 mri with transformers,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Lymph node detection in t2 mri with transformers,

Reference 26

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This paper cites Universal l ymph node detection in t2 mri using neural networks,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Universal l ymph node detection in t2 mri using neural networks,

Reference 27

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Observation 2fc1b51e-b23c-42d5-937e-ee9875aa9e69 · outbound

This paper cites Domain shift in computer vision models for M RI data analysis: an overview,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Domain shift in computer vision models for M RI data analysis: an overview,

Reference 28

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Observation d80889fc-67f2-4379-9fe6-7bb9a9799a19 · outbound

This paper cites A 3d unsupervised domain adaptation framework combining style translation a nd self- training for abdominal organs segmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation A 3d unsupervised domain adaptation framework combining style translation a nd self- training for abdominal organs segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.485434Z

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Observation cb1fe4cb-cec3-4195-b331-8f3d7166371b · outbound

This paper cites Unsupervi sed domain adaptation for abdominal organ segmentation using p seudo labels and organ attention cyclegan,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Unsupervi sed domain adaptation for abdominal organ segmentation using p seudo labels and organ attention cyclegan,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.474436Z

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Observation e74d26a8-40a7-4c86-b6a1-35b6ac7339cc · outbound

This paper cites Unpaired image -to- image translation using cycle-consistent adversarial net works,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Unpaired image -to- image translation using cycle-consistent adversarial net works,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.463470Z

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Observation 22a5968f-2738-4e25-93fa-4f6b5c4104c3 · outbound

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

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.451928Z

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Observation 8dac8db5-f886-4d10-a20e-6521309d9d2e · outbound

This paper cites Robust machine learning segmentation for large-scale ana lysis of heterogeneous clinical brain mri datasets,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Robust machine learning segmentation for large-scale ana lysis of heterogeneous clinical brain mri datasets,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.440620Z

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Observation ae14da53-d0c9-4ff3-9250-44fcc1eb8151 · outbound

This paper cites Domain randomization for transferring deep neural networ ks from simulation to the real world,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Domain randomization for transferring deep neural networ ks from simulation to the real world,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.429181Z

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Observation ea7d5fb6-819a-4138-8a6d-28a9858a9100 · outbound

This paper cites AMOS: A large-scale abdominal mult i- organ benchmark for versatile medical image segmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation AMOS: A large-scale abdominal mult i- organ benchmark for versatile medical image segmentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.416020Z

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Observation 1c13bdae-1e0f-4b24-9083-8724b321faaf · outbound

This paper cites CHAOS - combined (CT-MR) healthy abdominal organ segmentation cha llenge data,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation CHAOS - combined (CT-MR) healthy abdominal organ segmentation cha llenge data,

Reference 36

Resolution: verified exact; raw_fallback, observed 2026-08-06T14:41:57.085042Z

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Observation 787f4de0-9232-4bed-9547-590e10829079 · outbound

This paper cites Comparison of semi- automatic and deep learning-based automatic methods for li ver segmen- tation in living liver transplant donors,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Comparison of semi- automatic and deep learning-based automatic methods for li ver segmen- tation in living liver transplant donors,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.403291Z

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Observation b7530042-2a43-4647-b7ef-102248310bb2 · outbound

This paper cites CHAOS challenge- combined (CT-MR) healthy abdominal organ segmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation CHAOS challenge- combined (CT-MR) healthy abdominal organ segmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.391003Z

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Observation 72e0c473-22de-4ed8-b0dd-387a13babbf5 · outbound

This paper cites Liver- HCCSeg: A publicly available multiphasic MRI dataset with l iver and HCC tumor segmentations and inter-rater agreement analysi s,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Liver- HCCSeg: A publicly available multiphasic MRI dataset with l iver and HCC tumor segmentations and inter-rater agreement analysi s,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.377050Z

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Observation 304059ef-7002-4679-929b-c8f0862635e4 · outbound

This paper cites L iverHC- Ceg: A publicly available multiphasic MRI dataset with live r and HCCC tumor segmentations and inter-rater agreement analysis,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation L iverHC- Ceg: A publicly available multiphasic MRI dataset with live r and HCCC tumor segmentations and inter-rater agreement analysis,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.365099Z

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Observation 6a193a5b-a8d6-437f-8471-a45915d0ec4a · outbound

This paper cites Monai label: A fra mework for ai-assisted interactive labeling of 3D medical images,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Monai label: A fra mework for ai-assisted interactive labeling of 3D medical images,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.354368Z

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Observation f7ea22c9-8a7c-4a88-b957-6e764142c849 · outbound

This paper cites Segment ation of pelvic structures in T2 MRI via MR-to-CT synthesis,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Segment ation of pelvic structures in T2 MRI via MR-to-CT synthesis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.343158Z

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Observation b247789d-3259-4080-a0ff-a4b0acb4bb2b · outbound

This paper cites Fully convolutional neural netw orks for volumetric medical image segmentation,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Fully convolutional neural netw orks for volumetric medical image segmentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.332828Z

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Observation 8b55eb88-6134-4a90-a63e-937e45ff3e4f · outbound

This paper cites Jetstream2: Accelerating cloud computing via j etstream.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Jetstream2: Accelerating cloud computing via j etstream

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.321987Z

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Observation 48693700-1b30-4b4b-8bf0-05d2a460e037 · outbound

This paper cites ACC ESS: Advancing innovation: NSF’s advanced cyberinfrastructur e coordination ecosystem: Services & support,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation ACC ESS: Advancing innovation: NSF’s advanced cyberinfrastructur e coordination ecosystem: Services & support,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.309808Z

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Observation 4919b2cb-7dea-495f-8d43-ec43e7095dcb · outbound

This paper cites The cancer genome atlas liver hepatocellular carcinoma collec tion (TCGA-LIHC) (version 5) [data set],.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation The cancer genome atlas liver hepatocellular carcinoma collec tion (TCGA-LIHC) (version 5) [data set],

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.297078Z

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Observation 366e5308-6804-48ba-b696-97260763eeb0 · outbound

This paper cites Measures of the amount of ecologic association bet ween species,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Measures of the amount of ecologic association bet ween species,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.285080Z

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.

Observation 778f7af3-b9ff-486a-b52c-70cfa806fbeb · outbound

This paper cites Compari ng images using the hausdorff distance,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Compari ng images using the hausdorff distance,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.271848Z

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.

Observation 4fd21efd-9fd7-43ac-a9f0-048a0150f6d3 · outbound

This paper cites Segmentatio n precision of abdominal anatomy for mri-based radiotherapy ,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Segmentatio n precision of abdominal anatomy for mri-based radiotherapy ,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.258212Z

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

Observation 4bd27145-205d-4a0c-9ea7-52f373cd7da0 · outbound

This paper cites Pancreas segmenta tion in mri using graph-based decision fusion on convolutional n eural net- works,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Pancreas segmenta tion in mri using graph-based decision fusion on convolutional n eural net- works,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.246115Z

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

Observation d9b1c9ae-db4e-4fdd-9a84-67756adf773a · outbound

This paper cites Noise and the reality gap: The use of simulation in evolutionary robotics,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Noise and the reality gap: The use of simulation in evolutionary robotics,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.234694Z

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.

Observation 43318a0f-1e9f-4efd-baad-81bea275e0ce · outbound

This paper cites VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T14:41:56.919932Z

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Unavailable: canonical work link unavailable.

Observation fc331ae1-7cf5-40d2-b142-c3bb847c307e · outbound

This paper cites Maximum likelihood fro m incomplete data via the EM algorithm,.

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation Maximum likelihood fro m incomplete data via the EM algorithm,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:57.221458Z

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