Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models | Paper Citation Record

Observation 7c3ced76-31cf-418f-bbf6-fe47630c66f7 · outbound

This paper cites Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis: pathogenesis, symptoms, diagnoses and cell-based therapy

Reference 1

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This paper cites Review of multiple sclerosis: Epidemiology, etiology, pathophysiology, and treatment.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Review of multiple sclerosis: Epidemiology, etiology, pathophysiology, and treatment

Reference 2

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This paper cites Evidence-based guidelines: Magnims consensus guidelines on the use of mri in multiple sclerosis-establishing disease prognosis and monitoring patients.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Evidence-based guidelines: Magnims consensus guidelines on the use of mri in multiple sclerosis-establishing disease prognosis and monitoring patients

Reference 3

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This paper cites nnu-net: Self-adapting framework for u-net-based medical image segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models nnu-net: Self-adapting framework for u-net-based medical image segmentation

Reference 4

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Observation c7f16419-b80a-4b10-892f-f35f3b3697ce · outbound

This paper cites 3d mri brain tumor segmentation using autoencoder regularization.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models 3d mri brain tumor segmentation using autoencoder regularization

Reference 5

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This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 6

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This paper cites Segmentation of multiple sclerosis lesions in intensity corrected multispectral mri.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Segmentation of multiple sclerosis lesions in intensity corrected multispectral mri

Reference 7

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Observation ff57739e-4fb4-4a56-98f5-bfa5262cb9ee · outbound

This paper cites Spatial decision forests for ms lesion segmentation in multi-channel mr images.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Spatial decision forests for ms lesion segmentation in multi-channel mr images

Reference 8

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Observation 6d3f112b-ebe2-4e95-8bc4-00a88a91c199 · outbound

This paper cites Multi-sectional views textural based svm for ms lesion segmentation in multi-channels mris.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multi-sectional views textural based svm for ms lesion segmentation in multi-channels mris

Reference 9

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Observation 07205ef5-dc50-42c2-91cd-c53298c7b4c5 · outbound

This paper cites Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation

Reference 10

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This paper cites Mri flair lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Mri flair lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?

Reference 11

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This paper cites Two time point ms lesion segmentation in brain mri: An expectation-maximization framework.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Two time point ms lesion segmentation in brain mri: An expectation-maximization framework

Reference 12

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This paper cites Improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach

Reference 13

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This paper cites Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Reference 14

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This paper cites Multi-view longitudinal cnn for multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multi-view longitudinal cnn for multiple sclerosis lesion segmentation

Reference 15

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Observation 3702b5f1-1f6a-4b9f-9ebc-bd5fbcbeb971 · outbound

This paper cites Neural network-based learning kernel for auto- matic segmentation of multiple sclerosis lesions on magnetic resonance images.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Neural network-based learning kernel for auto- matic segmentation of multiple sclerosis lesions on magnetic resonance images

Reference 16

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This paper cites Multi-branch convolutional neural network for multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

Reference 17

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Observation fcb703ba-750c-4bef-8df7-d8b4ca031707 · outbound

This paper cites Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure

Reference 18

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This paper cites Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study

Reference 19

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This paper cites Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation

Reference 20

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This paper cites Multiple sclerosis lesion segmentation with tiramisu and 2.5d stacked slices.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis lesion segmentation with tiramisu and 2.5d stacked slices

Reference 21

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Observation 0edffa90-76b8-4a8f-ab79-2e024b329ad1 · outbound

This paper cites RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation

Reference 22

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Observation c90391a9-55c7-401e-be1e-16b7d17cd5b5 · outbound

This paper cites Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

Reference 23

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This paper cites A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

Reference 24

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This paper cites Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation

Reference 25

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This paper cites State-of-the-art segmentation techniques and future directions for multiple sclerosis brain lesions.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models State-of-the-art segmentation techniques and future directions for multiple sclerosis brain lesions

Reference 26

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This paper cites All-net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models All-net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Reference 27

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This paper cites Multiple sclerosis lesion analysis in brain magnetic resonance images: Techniques and clinical applications.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis lesion analysis in brain magnetic resonance images: Techniques and clinical applications

Reference 28

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This paper cites Multiple sclerosis lesion segmentation in brain mri using inception modules embedded in a convolutional neural network.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis lesion segmentation in brain mri using inception modules embedded in a convolutional neural network

Reference 29

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This paper cites Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks

Reference 30

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This paper cites An open-source tool for longitudinal whole-brain and white matter lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models An open-source tool for longitudinal whole-brain and white matter lesion segmentation

Reference 31

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This paper cites Framework to segment and evaluate multiple sclerosis lesion in mri slices using vgg-unet.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Framework to segment and evaluate multiple sclerosis lesion in mri slices using vgg-unet

Reference 32

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This paper cites Multiple sclerosis lesions segmentation using attention-based cnns in flair images.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis lesions segmentation using attention-based cnns in flair images

Reference 33

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Observation 3ab30e71-6b5e-4508-bde5-df546bbaef84 · outbound

This paper cites Noise invariant convolution neural network for segmentation of multiple sclerosis lesions from brain magnetic resonance imaging.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Noise invariant convolution neural network for segmentation of multiple sclerosis lesions from brain magnetic resonance imaging

Reference 34

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Observation 7072d406-f1b0-4280-bfa0-2f08ac7131f7 · outbound

This paper cites Triplanar u-net with lesion-wise voting for the segmentation of new lesions on longitudinal mri studies.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Triplanar u-net with lesion-wise voting for the segmentation of new lesions on longitudinal mri studies

Reference 35

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This paper cites Using convolutional neural networks for segmen- tation of multiple sclerosis lesions in 3d magnetic resonance imaging.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Using convolutional neural networks for segmen- tation of multiple sclerosis lesions in 3d magnetic resonance imaging

Reference 36

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Observation 274218ce-4f3d-4935-8b28-7d09c277b425 · outbound

This paper cites Boosting multiple sclerosis lesion segmentation through attention mechanism.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Boosting multiple sclerosis lesion segmentation through attention mechanism

Reference 37

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Observation c9ea75d0-6aa4-47d9-b66b-8459e54ea93d · outbound

This paper cites Coactseg: Learning from heterogeneous data for new multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Coactseg: Learning from heterogeneous data for new multiple sclerosis lesion segmentation

Reference 38

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This paper cites Enhancing multiple sclerosis lesion segmentation Fig. 5.nnU-Net Performance Evaluation on MSLesSeg: The same structure as Figure 4 in multimodal mri scans with diffusion models.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Enhancing multiple sclerosis lesion segmentation Fig. 5.nnU-Net Performance Evaluation on MSLesSeg: The same structure as Figure 4 in multimodal mri scans with diffusion models

Reference 39

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Observation c501d5ac-087a-4ccf-beed-068d59a8e7e6 · outbound

This paper cites Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

Reference 40

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This paper cites Scanner agnostic large-scale evaluation of ms lesion delineation tool for clinical mri.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Scanner agnostic large-scale evaluation of ms lesion delineation tool for clinical mri

Reference 41

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Observation 5bc899bd-47df-45bb-8e0f-3c6f1400c93d · outbound

This paper cites Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified u-net model.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified u-net model

Reference 42

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Observation a2782353-e77a-4151-9caa-caf3732f4a22 · outbound

This paper cites Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.254046Z

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Observation b529e66f-7290-4fcc-8fda-b67b4376a27c · outbound

This paper cites Consensus of algorithms for lesion segmentation in brain mri studies of multiple sclerosis.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Consensus of algorithms for lesion segmentation in brain mri studies of multiple sclerosis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.348577Z

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Observation 3ad363e0-3ac6-40e7-b2bd-98a55f0bf069 · outbound

This paper cites Diagnosis of multiple sclerosis lesion using deep learning models.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Diagnosis of multiple sclerosis lesion using deep learning models

Reference 45

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Observation fcc6e8df-4e33-477f-b31d-62297cae280d · outbound

This paper cites Icpr 2024 competition on multiple sclerosis lesion segmentation - methods and results.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Icpr 2024 competition on multiple sclerosis lesion segmentation - methods and results

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.334683Z

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Observation 7fd5717f-6eda-488d-8338-a29e7975093f · outbound

This paper cites Longitudinal segmentation of ms lesions via temporal difference weighting.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Longitudinal segmentation of ms lesions via temporal difference weighting

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.257506Z

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Observation c7d721a2-20a7-470a-bb40-0395f30f9031 · outbound

This paper cites Lst-ai: A deep learning ensemble for accurate ms lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Lst-ai: A deep learning ensemble for accurate ms lesion segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.341493Z

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Observation 1178d59b-f19b-402a-b310-425489840dce · outbound

This paper cites SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:21:21.708632Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation db6b57be-be5c-4688-a87f-8565c43bd122 · outbound

This paper cites SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:21:21.717050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 92327727-ef77-48b9-8510-5adbcd3c629f · outbound

This paper cites A novel convolutional neural network for automated multiple sclerosis brain lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models A novel convolutional neural network for automated multiple sclerosis brain lesion segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.281127Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59d90568-b8aa-4846-b0a8-6800b11606ae · outbound

This paper cites Enhancing precision in multiple sclerosis lesion segmentation: A u-net based machine learning approach with data augmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Enhancing precision in multiple sclerosis lesion segmentation: A u-net based machine learning approach with data augmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.231780Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52530462-2c9a-40cc-86a8-6ba74fbb0f9d · outbound

This paper cites Flames: A robust deep learning model for automated multiple sclerosis lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Flames: A robust deep learning model for automated multiple sclerosis lesion segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.228572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae0fbab9-b06f-478d-b91f-00e5fe33438e · outbound

This paper cites Mslesseg: baseline and benchmarking of a new multiple sclerosis lesion segmentation dataset.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Mslesseg: baseline and benchmarking of a new multiple sclerosis lesion segmentation dataset

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.344955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ef38e8b-c9b8-42ff-98fc-d5206131f2d9 · outbound

This paper cites Enhanced segmentation of active and nonactive mul- tiple sclerosis plaques in t1 and flair mri images using transformer-based encoders.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Enhanced segmentation of active and nonactive mul- tiple sclerosis plaques in t1 and flair mri images using transformer-based encoders

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.337842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bda0bbed-5b91-4934-9efc-fd94a61d9533 · outbound

This paper cites Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.270612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33109714-44d4-435f-9efa-e7c6f76e18e5 · outbound

This paper cites Multi-scale convolutional-stack aggregation for robust white matter hyperintensities segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Multi-scale convolutional-stack aggregation for robust white matter hyperintensities segmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.365290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d4d2b1f-c1c5-4f88-b865-06e59ed3bb0e · outbound

This paper cites Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.369495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87dd27c5-b813-4e5f-a753-dd63b2767052 · outbound

This paper cites Lst-ai: a deep learning ensemble for accurate ms lesion segmentation.

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models Lst-ai: a deep learning ensemble for accurate ms lesion segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T15:31:40.373662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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