{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/mri-segmentation/papers/2","list_of":"/task/mri-segmentation","task":"MRI segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,177],"of":177,"counts":{"archive_papers_tagged":177,"with_a_code_link":68,"where_syntology_ran_a_sample":3,"not_listed_spam_title":0,"listed":177,"listed_where_code_ran":3,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":2,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/mri-segmentation","prev":"/task/mri-segmentation","next":null,"papers":[{"url":null,"slug":"uncertainty-aware-ai-for-mri-segmentation","title":"Bayesian Neural Networks for 2D MRI Segmentation","date":"2023-11-24","arxiv_id":"2311.14875","repositories_listed":0,"syntology":null},{"url":null,"slug":"pelvic-floor-mri-segmentation-based-on-semi","title":"Pelvic floor MRI segmentation based on semi-supervised deep learning","date":"2023-11-06","arxiv_id":"2311.03105","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-danet-v2-a-flexible-dynamic-architecture","title":"Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation","date":"2023-10-28","arxiv_id":"2310.18656","repositories_listed":0,"syntology":null},{"url":null,"slug":"competitive-ensembling-teacher-student","title":"Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation","date":"2023-10-21","arxiv_id":"2310.13955","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-cardiac-mri-segmentation-via","title":"Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer","date":"2023-09-02","arxiv_id":"2309.00800","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multi-view-data-without","title":"Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach","date":"2023-08-12","arxiv_id":"2308.06477","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-based-unsupervised-domain","title":"Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging","date":"2023-08-08","arxiv_id":"2308.04395","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-mri-segmentation-using-template-based","title":"Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation","date":"2023-08-04","arxiv_id":"2308.02363","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-annotation-strategies-for-segmentation","title":"Sparse annotation strategies for segmentation of short axis cardiac MRI","date":"2023-07-24","arxiv_id":"2307.12619","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-intervals-for-performance","title":"Confidence Intervals for Performance Estimates in Brain MRI Segmentation","date":"2023-07-20","arxiv_id":"2307.10926","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-weighted-logit-consistency-improves","title":"Boundary-weighted logit consistency improves calibration of segmentation networks","date":"2023-07-16","arxiv_id":"2307.08163","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-novel-diagnostic","title":"Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis","date":"2023-05-30","arxiv_id":"2305.18841","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-asnr-miccai-brain-tumor-segmentation","title":"The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma","date":"2023-05-12","arxiv_id":"2305.07642","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-agreement-from-multi-source","title":"Learning Robust Medical Image Segmentation from Multi-source Annotations","date":"2023-04-02","arxiv_id":"2304.00466","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-for-adaptive-mri","title":"Knowledge Distillation for Adaptive MRI Prostate Segmentation Based on Limit-Trained Multi-Teacher Models","date":"2023-03-16","arxiv_id":"2303.09494","repositories_listed":0,"syntology":null},{"url":null,"slug":"supermask-generating-high-resolution-object","title":"SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs","date":"2023-03-13","arxiv_id":"2303.07517","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-evaluation-of-vanilla-residual","title":"Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans","date":"2022-11-29","arxiv_id":"2211.16570","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-generalization-in-fetal-brain-mri","title":"Domain generalization in fetal brain MRI segmentation \\\\with multi-reconstruction augmentation","date":"2022-11-25","arxiv_id":"2211.14282","repositories_listed":0,"syntology":null},{"url":null,"slug":"fighting-the-scanner-effect-in-brain-mri","title":"Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data","date":"2022-11-04","arxiv_id":"2211.02400","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-validation-of-ai-and-non-ai","title":"Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes","date":"2022-07-23","arxiv_id":"2207.11534","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-contrast-mri-segmentation-trained-on","title":"Multi-Contrast MRI Segmentation Trained on Synthetic Images","date":"2022-07-06","arxiv_id":"2207.02469","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-danet-dynamic-architecture-network-for","title":"Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation","date":"2022-06-14","arxiv_id":"2206.06575","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-modeling-of-image-and-label-statistics","title":"Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation","date":"2022-06-09","arxiv_id":"2206.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"rt-dnas-real-time-constrained-differentiable","title":"RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation","date":"2022-06-08","arxiv_id":"2206.04682","repositories_listed":0,"syntology":null},{"url":null,"slug":"parotid-gland-mri-segmentation-based-on-swin","title":"Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images","date":"2022-06-07","arxiv_id":"2206.03336","repositories_listed":0,"syntology":null},{"url":null,"slug":"act-semi-supervised-domain-adaptive-medical","title":"ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training","date":"2022-06-05","arxiv_id":"2206.02288","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-meets-transfer-learning","title":"Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation","date":"2022-06-03","arxiv_id":"2206.01369","repositories_listed":0,"syntology":null},{"url":null,"slug":"corps-cost-free-rigorous-pseudo-labeling","title":"CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation","date":"2022-05-19","arxiv_id":"2205.09601","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-quantifying-inter","title":"Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures","date":"2022-01-18","arxiv_id":"2201.07106","repositories_listed":0,"syntology":null},{"url":null,"slug":"vitbis-vision-transformer-for-biomedical","title":"ViTBIS: Vision Transformer for Biomedical Image Segmentation","date":"2022-01-15","arxiv_id":"2201.05920","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastsurfervinn-building-resolution","title":"FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI","date":"2021-12-17","arxiv_id":"2112.09654","repositories_listed":0,"syntology":null},{"url":null,"slug":"acquisition-invariant-brain-mri-segmentation","title":"Acquisition-invariant brain MRI segmentation with informative uncertainties","date":"2021-11-07","arxiv_id":"2111.04094","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-mada-unsupervised-cross-modality","title":"C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation","date":"2021-10-29","arxiv_id":"2110.15823","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-decoupled-uncertainty-model-for-mri","title":"A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation","date":"2021-09-06","arxiv_id":"2109.02413","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-shoulder-muscle-mri-using-a","title":"Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder","date":"2021-08-26","arxiv_id":"2108.11720","repositories_listed":0,"syntology":null},{"url":"/paper/3d-agse-vnet-an-automatic-brain-tumor-mri","slug":"3d-agse-vnet-an-automatic-brain-tumor-mri","title":"3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework","date":"2021-07-26","arxiv_id":"2107.12046","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-cardiac-mri-segmentation","title":"Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?","date":"2021-07-23","arxiv_id":"2107.11447","repositories_listed":0,"syntology":null},{"url":null,"slug":"vtbis-vision-transformer-for-biomedical-image","title":"VTBIS: Vision Transformer for Biomedical Image Segmentation","date":"2021-07-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dfenet-a-novel-dimension-fusion-edge-guided","title":"DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation","date":"2021-05-17","arxiv_id":"2105.07962","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretability-of-a-deep-learning-model-in","title":"Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset","date":"2021-03-15","arxiv_id":"2103.08590","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-3d-information-in-unsupervised","title":"Leveraging 3D Information in Unsupervised Brain MRI Segmentation","date":"2021-01-26","arxiv_id":"2101.10674","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-uncertainty-in-neural-networks-for","title":"Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study","date":"2020-12-31","arxiv_id":"2012.15772","repositories_listed":0,"syntology":null},{"url":null,"slug":"softseg-advantages-of-soft-versus-binary","title":"SoftSeg: Advantages of soft versus binary training for image segmentation","date":"2020-11-18","arxiv_id":"2011.09041","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-cardiac-intervention-assistance","title":"Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation","date":"2020-08-17","arxiv_id":"2008.07071","repositories_listed":0,"syntology":null},{"url":null,"slug":"ica-unet-ica-inspired-statistical-unet-for","title":"ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation","date":"2020-07-18","arxiv_id":"2007.09455","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-generative-model-based-quality-control","title":"Deep Generative Model-based Quality Control for Cardiac MRI Segmentation","date":"2020-06-23","arxiv_id":"2006.13379","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-pipeline-for-error","title":"A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results","date":"2020-05-28","arxiv_id":"2005.13987","repositories_listed":0,"syntology":null},{"url":null,"slug":"sau-net-efficient-3d-spine-mri-segmentation","title":"SAU-Net: Efficient 3D Spine MRI Segmentation Using Inter-Slice Attention","date":"2020-01-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"infant-brain-mri-segmentation-with-dilated","title":"Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention","date":"2019-12-29","arxiv_id":"1912.12570","repositories_listed":0,"syntology":null},{"url":null,"slug":"191209847","title":"Transfer Learning with Edge Attention for Prostate MRI Segmentation","date":"2019-12-20","arxiv_id":"1912.09847","repositories_listed":0,"syntology":null},{"url":null,"slug":"assemblynet-a-large-ensemble-of-cnns-for-3d","title":"AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation","date":"2019-11-20","arxiv_id":"1911.09098","repositories_listed":0,"syntology":null},{"url":null,"slug":"scanner-invariant-multiple-sclerosis-lesion","title":"Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI","date":"2019-10-22","arxiv_id":"1910.10035","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-deep-affine-invariant-shape-learning-for","title":"3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation","date":"2019-09-14","arxiv_id":"1909.06629","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-multi-sequence-and-synthetic-images","title":"Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI","date":"2019-09-03","arxiv_id":"1909.01182","repositories_listed":0,"syntology":null},{"url":null,"slug":"skip-connected-3d-densenet-for-volumetric","title":"Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-rodent-brain-mri-lesion","title":"Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks","date":"2019-08-23","arxiv_id":"1908.08746","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-multi-sequence-cardiac-mri","title":"Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation","date":"2019-08-21","arxiv_id":"1908.07726","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-brain-magnetic-resonance-image-mri","title":"Improving Brain Magnetic Resonance Image MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth","date":"2019-07-22","arxiv_id":"1907.09505","repositories_listed":0,"syntology":null},{"url":null,"slug":"assemblynet-a-novel-deep-decision-making","title":"AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation","date":"2019-06-05","arxiv_id":"1906.01862","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-objective-optimization-approach-for","title":"A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods","date":"2019-05-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"just-enough-interaction-approach-to-knee-mri","title":"Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative","date":"2019-03-10","arxiv_id":"1903.04027","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-cost-functions-for-3d-and-4d","title":"Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative","date":"2019-03-10","arxiv_id":"1903.03927","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-mri-segmentation-using-rule-based","title":"Brain MRI Segmentation using Rule-Based Hybrid Approach","date":"2019-02-12","arxiv_id":"1902.04207","repositories_listed":0,"syntology":null},{"url":null,"slug":"healthy-versus-pathological-learning","title":"Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders","date":"2019-01-06","arxiv_id":"1901.01620","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-method-of-multimodal-mri-brain-image","title":"The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features","date":"2018-11-10","arxiv_id":"1811.04281","repositories_listed":0,"syntology":null},{"url":null,"slug":"infinet-fully-convolutional-networks-for","title":"InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation","date":"2018-10-11","arxiv_id":"1810.05735","repositories_listed":0,"syntology":null},{"url":null,"slug":"exclusive-independent-probability-estimation","title":"Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation","date":"2018-09-21","arxiv_id":"1809.08168","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-probabilistic-atlas-of-the-human-thalamic","title":"A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology","date":"2018-06-22","arxiv_id":"1806.08634","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-cs-mri-reconstruction-and-segmentation","title":"Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network","date":"2018-05-06","arxiv_id":"1805.02165","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-aided-knee-joint-magnetic-resonance","title":"Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey","date":"2018-02-13","arxiv_id":"1802.04894","repositories_listed":0,"syntology":null},{"url":null,"slug":"mri-cross-modality-neuroimage-to-neuroimage","title":"MRI Cross-Modality NeuroImage-to-NeuroImage Translation","date":"2018-01-22","arxiv_id":"1801.06940","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stream-3d-fcn-with-multi-scale-deep","title":"Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation","date":"2017-11-28","arxiv_id":"1711.10212","repositories_listed":0,"syntology":null},{"url":null,"slug":"isointense-infant-brain-mri-segmentation-with","title":"Isointense infant brain MRI segmentation with a dilated convolutional neural network","date":"2017-08-09","arxiv_id":"1708.02757","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-and-dilated-convolutions","title":"Adversarial training and dilated convolutions for brain MRI segmentation","date":"2017-07-11","arxiv_id":"1707.03195","repositories_listed":0,"syntology":null},{"url":null,"slug":"-net-deep-learning-for-generalized","title":"$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters","date":"2017-06-14","arxiv_id":"1706.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-detection-based-on-bilateral","title":"Brain Tumor Detection Based on Bilateral Symmetry Information","date":"2014-12-09","arxiv_id":"1412.3009","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-mri-segmentation-with-fast-and-globally","title":"Brain MRI Segmentation with Fast and Globally Convex Multiphase Active Contours","date":"2013-08-28","arxiv_id":"1308.6056","repositories_listed":0,"syntology":null}],"record_sha256":"5b7d162243ee5f7e7fd8a7e136bd6991ccde631d805c262665201eb1f9b6e271","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}