Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline | Paper Citation Record

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This paper cites Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

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This paper cites Ultrasound Nerve Segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Ultrasound Nerve Segmentation

Reference 2

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This paper cites Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

Reference 4

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Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Unresolved cited work

Reference 5

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This paper cites The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 6

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This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 7

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This paper cites Kvasir-seg: A segmented polyp dataset.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Kvasir-seg: A segmented polyp dataset

Reference 8

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This paper cites Evaluation of three algorithms for the segmentation of overlapping cervical cells.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Evaluation of three algorithms for the segmentation of overlapping cervical cells

Reference 9

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This paper cites An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells

Reference 10

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This paper cites 2017 Robotic Instrument Segmentation Challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline 2017 Robotic Instrument Segmentation Challenge

Reference 11

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This paper cites ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI

Reference 12

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This paper cites Algorithms for left atrial wall segmentation and thickness–evaluation on an open- source CT and MRI image database.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Algorithms for left atrial wall segmentation and thickness–evaluation on an open- source CT and MRI image database

Reference 13

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This paper cites MS Lesion Segmentation Challenge 2008.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline MS Lesion Segmentation Challenge 2008

Reference 15

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This paper cites Evaluation of three-dimensional finite element-based deformable registration of pre-and intraoperative prostate imaging.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Evaluation of three-dimensional finite element-based deformable registration of pre-and intraoperative prostate imaging

Reference 16

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This paper cites Joint optic disc and cup segmentation based on multi-label deep network and polar transformation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Joint optic disc and cup segmentation based on multi-label deep network and polar transformation

Reference 17

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This paper cites Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation

Reference 18

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Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Unresolved cited work

Reference 19

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This paper cites Nanonet: Real-time polyp segmentation in video capsule endoscopy and colonoscopy.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Nanonet: Real-time polyp segmentation in video capsule endoscopy and colonoscopy

Reference 20

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This paper cites Dataset of breast ultrasound images. Data Brief 28, 104863 (2020).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)

Reference 21

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This paper cites RIM-ONE: Retinal Imaging - Medimrg.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline RIM-ONE: Retinal Imaging - Medimrg

Reference 22

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This paper cites Age challenge: angle closure glaucoma evaluation in anterior segment optical coherence tomography.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Age challenge: angle closure glaucoma evaluation in anterior segment optical coherence tomography

Reference 24

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This paper cites Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge

Reference 25

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This paper cites Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge

Reference 26

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This paper cites A whole-body FDG-PET/CT dataset with manually annotated tumor lesions.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A whole-body FDG-PET/CT dataset with manually annotated tumor lesions

Reference 27

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This paper cites WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

Reference 28

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This paper cites Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge

Reference 29

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This paper cites VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images

Reference 30

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This paper cites A vertebral segmentation dataset with fracture grading.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A vertebral segmentation dataset with fracture grading

Reference 31

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This paper cites Comparing algorithms for automated vessel segmentation in computed tomogra- phy scans of the lung: the VESSEL12 study.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Comparing algorithms for automated vessel segmentation in computed tomogra- phy scans of the lung: the VESSEL12 study

Reference 32

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This paper cites Automatic nerve segmentation of ultrasound images.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Automatic nerve segmentation of ultrasound images

Reference 33

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This paper cites TotalSegmentator: robust segmentation of 104 anatomic structures in CT images.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline TotalSegmentator: robust segmentation of 104 anatomic structures in CT images

Reference 34

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This paper cites Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning

Reference 35

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This paper cites Retrieved fromhttps://structseg2019.grand-challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Retrieved fromhttps://structseg2019.grand-challenge

Reference 36

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This paper cites Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Reference 37

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This paper cites Multi-site, multi-domain airway tree modeling.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Multi-site, multi-domain airway tree modeling

Reference 38

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This paper cites Abdomenct-1k: Is abdominal organ segmentation a solved problem?.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Abdomenct-1k: Is abdominal organ segmentation a solved problem?

Reference 39

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This paper cites A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Reference 40

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This paper cites Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge

Reference 41

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This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 42

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This paper cites The multimodal brain tumor image segmentation benchmark (BRATS).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The multimodal brain tumor image segmentation benchmark (BRATS)

Reference 43

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This paper cites The virtual skeleton database: an open access repository for biomedical research and collaboration.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The virtual skeleton database: an open access repository for biomedical research and collaboration

Reference 44

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This paper cites Advancing the cancer genome atlas glioma MRI collections with expert segmenta- tion labels and radiomic features.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Advancing the cancer genome atlas glioma MRI collections with expert segmenta- tion labels and radiomic features

Reference 45

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This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 46

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This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 47

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This paper cites Neural segmentation of seeding ROIs (sROIs) for pre-surgical brain tractography.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Neural segmentation of seeding ROIs (sROIs) for pre-surgical brain tractography

Reference 48

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This paper cites Automatic segmentation of white matter tracts using multiple brain MRI sequences.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Automatic segmentation of white matter tracts using multiple brain MRI sequences

Reference 49

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This paper cites http://www.cad-pe.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline http://www.cad-pe

Reference 50

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This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Reference 51

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This paper cites Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Reference 52

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This paper cites Pathological myopic image analysis with transfer learning.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Pathological myopic image analysis with transfer learning

Reference 53

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This paper cites ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Reference 54

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This paper cites A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard

Reference 55

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This paper cites Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge

Reference 56

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This paper cites ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Reference 57

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This paper cites The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

Reference 58

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This paper cites A stochastic polygons model for glandular structures in colon histology images.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A stochastic polygons model for glandular structures in colon histology images

Reference 59

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This paper cites Gland segmentation in colon histology images: The glas challenge contest.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Gland segmentation in colon histology images: The glas challenge contest

Reference 60

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Observation a6698fb7-adba-4b4b-8a40-49ff377ad1bb · outbound

This paper cites Hematoma expansion context guided intracranial hemorrhage segmentation and uncertainty estimation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Hematoma expansion context guided intracranial hemorrhage segmentation and uncertainty estimation

Reference 61

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Observation e2753777-b0a5-4be5-9d61-9a2b9788ff66 · outbound

This paper cites Meta grayscale adaptive network for 3D integrated renal structures segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Meta grayscale adaptive network for 3D integrated renal structures segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.402112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4d86a829-02c3-4b31-b298-8cf8a643849e · outbound

This paper cites Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.386700Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a6e29415-7b69-49e5-9008-e704ee554329 · outbound

This paper cites Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.371857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6d2dd4c1-a100-4ff3-af62-316875f4646a · outbound

This paper cites Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.356902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5af762cb-8b69-4aa6-bc3a-1ba48c034ed9 · outbound

This paper cites The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:08.684647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34427221-ec41-4873-84fa-1c425be15117 · outbound

This paper cites A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.338300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ef70dbb-7cf7-4ea1-b568-3a48023a3700 · outbound

This paper cites AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.322845Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 085cdf71-c002-4b67-b08c-36d47e54ed9e · outbound

This paper cites Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.306047Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb1ea870-4d07-44cf-a222-776cbdc4c89c · outbound

This paper cites AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.291969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b50d366-3782-4c3d-b822-0c3e3281f572 · outbound

This paper cites LNDb: A Lung Nodule Database on Computed Tomography.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline LNDb: A Lung Nodule Database on Computed Tomography

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:08.709576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6b7c12b-d641-4e78-847f-c8fa53a5be4a · outbound

This paper cites Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.276056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c0852eca-9392-4cc2-a64c-2972d2e8026f · outbound

This paper cites The liver tumor segmentation benchmark (lits).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The liver tumor segmentation benchmark (lits)

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.262101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d986e76-b808-4401-81a8-92289bf9c40d · outbound

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

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Longitudinal multiple sclerosis lesion segmentation: resource and challenge

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.247519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b0776a7b-7098-4233-94a7-174ab094565b · outbound

This paper cites Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.232979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40cd1099-6fcf-4664-8bde-d8dfac92a49e · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source im- ages.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Multivariate mixture model for myocardial segmentation combining multi-source im- ages

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.214034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c1e9ba75-62e0-434b-9db4-76a6b01c599f · outbound

This paper cites Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.199512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 224df472-7f84-4036-bbca-8db5415382be · outbound

This paper cites X -Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline X -Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.185285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 860143f4-9cd9-40c3-91b5-adb246e9f081 · outbound

This paper cites MRBrainS challenge: online evaluation framework for brain image segmen- tation in 3T MRI scans.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline MRBrainS challenge: online evaluation framework for brain image segmen- tation in 3T MRI scans

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.171849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2d8860ad-4673-4648-bc62-180b47899b99 · outbound

This paper cites 3D segmentation in the clinic: A grand challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline 3D segmentation in the clinic: A grand challenge

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.158728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fd3d8a58-87dd-4b3a-a674-c4e5ee30f29d · outbound

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

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.144518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation abee5001-d3f3-4331-a397-7a9b8ef6105c · outbound

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

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline CHAOS-combined (CT-MR) healthy abdominal organ segmentation challenge data

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.130032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2db37d42-5c76-48e9-9a7c-5f9a11fe71b1 · outbound

This paper cites Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.112932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aef8ac47-4bb4-4d53-ad9e-02e5ca3ad021 · outbound

This paper cites The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion).

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:14:09.375384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ccc6a44-bf2e-4ff1-997f-77ba8807d931 · outbound

This paper cites Adam challenge: Detecting age-related macular degeneration from fundus images.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Adam challenge: Detecting age-related macular degeneration from fundus images

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.097677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 84cfb528-8be6-49c4-b0f6-9e513e494879 · outbound

This paper cites Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.085601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e0962da-cf61-49b5-8196-947fed16fb1e · outbound

This paper cites Interactive whole-heart segmentation in congenital heart disease.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Interactive whole-heart segmentation in congenital heart disease

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.069529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf197324-4cf2-4076-b15f-33156bbfefed · outbound

This paper cites FUSeg: The foot ulcer segmentation challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline FUSeg: The foot ulcer segmentation challenge

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.055558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7358df99-1bc0-4e5b-9b8b-e4b481e3de96 · outbound

This paper cites An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.040608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e0a72f2-3d77-4739-81dd-08ea533bc0cf · outbound

This paper cites Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:08.944853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 581888c6-52d0-4618-b027-52e05186efb6 · outbound

This paper cites Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.026662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24e04bb4-d56e-4392-acdc-592bd7665902 · outbound

This paper cites MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:08.956571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbe3eb43-acf5-4fc0-8621-312349089ea7 · outbound

This paper cites Chest Image Dataset for Pneumothorax Segmentation.https://tianchi.aliyun.com/ dataset/83075, 2020.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Chest Image Dataset for Pneumothorax Segmentation.https://tianchi.aliyun.com/ dataset/83075, 2020

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:10.012553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a9a96706-ec31-46a7-96be-bb3e2d7227a2 · outbound

This paper cites Endoscopy disease detection challenge 2020.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Endoscopy disease detection challenge 2020

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:08.967411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5a83969-c332-4925-a076-24cd2c0c554b · outbound

This paper cites Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.996906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4b1016c-7a5c-4b8e-a6f5-fbab8c0be15e · outbound

This paper cites Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.982235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2719ca26-b0e7-445c-9594-11ae106c3468 · outbound

This paper cites A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.961913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ffea6bc-9ebf-4213-8fb6-579f3bbf03e2 · outbound

This paper cites Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.944567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4446fc02-561e-4737-bf6e-9d47b669bb8f · outbound

This paper cites CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.932498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9b583102-195d-4caa-9dc4-4ea367d716b1 · outbound

This paper cites Cross-modality brain structures image segmentation for the radiotherapy target definition and plan optimization.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Cross-modality brain structures image segmentation for the radiotherapy target definition and plan optimization

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.916121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d44683a-0758-48ff-bc0e-6f104d8fdc6c · outbound

This paper cites Computed tomography images for intracranial hemorrhage detection and segmentation.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Computed tomography images for intracranial hemorrhage detection and segmentation

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.900131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0a43b1dd-beee-4f40-96ca-0dabf3cffbb5 · outbound

This paper cites WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline WM-DOV A maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:14:09.886475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dc9e33f8-9926-4110-9166-fffd76dbf87e · outbound

This paper cites CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-12T17:14:09.010899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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