{"doi":"10.1007/978-3-031-43993-3_40","title":"TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation","abstract":null,"journal":"Lecture Notes in Computer Science","year":2023,"id":646949,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":663815,"name":"Yuqian Chen","orcid":"0009-0005-5613-2920","position":1,"is_corresponding":false},{"id":364867,"name":"Chaoyi Zhang","orcid":"0000-0001-8492-9711","position":2,"is_corresponding":false},{"id":273471,"name":"Alexandra J. Golby","orcid":"0000-0001-8461-9561","position":3,"is_corresponding":false},{"id":63230,"name":"Nikos Makris","orcid":"0000-0003-0425-3315","position":4,"is_corresponding":false},{"id":273470,"name":"Yogesh Rathi","orcid":"0000-0002-9946-2314","position":5,"is_corresponding":false},{"id":294938,"name":"Weidong Cai","orcid":"0000-0001-9581-7774","position":6,"is_corresponding":false},{"id":1338636,"name":"Fan Zhang","orcid":"0009-0006-6245-0199","position":7,"is_corresponding":false},{"id":273472,"name":"Lauren J. O’Donnell","orcid":"0000-0003-0197-7801","position":8,"is_corresponding":false},{"id":664340,"name":"Tengfei Xue","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation","abstract":"Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for large-scale datasets. Recently, deep-learning-based methods have been proposed for tractography parcellation using various types of representations for streamlines. However, these methods only focus on the information from a single streamline, ignoring geometric relationships between the streamlines in the brain. We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space. We propose a novel, learnable, local-global streamline representation that leverages information from neighboring and whole-brain streamlines to describe the local anatomy and global pose of the brain. We train our framework on a large-scale labeled tractography dataset, which we augment by applying synthetic transforms including rotation, scaling, and translations. We test our framework on five independently acquired datasets across populations and health conditions. TractCloud significantly outperforms several state-of-the-art methods on all testing datasets. TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration. The robustness and high inference speed of TractCloud make it suitable for large-scale tractography data analysis. Our project page is available at https://tractcloud.github.io/.","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4387211013","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"5R01MH132610-02","title":"Mapping of the intrinsic and extrinsic cerebellar connectome at ultra high resolution with expert neuroanatomical curation"},{"funder_name":"National Institutes of Health","grant_id":"5R01NS125781-03","title":"Quantitative Glioblastoma Margin and Infiltration Mapping with advanced diffusion-relaxation MRI"},{"funder_name":"National Institutes of Health","grant_id":"5R01MH074794-12","title":"Novel Diffusion MRI in Early Psychosis"},{"funder_name":"National Institutes of Health","grant_id":"1R01MH125860-01","title":"Mapping the superficial white matter connectome of the human brain using ultra high resolution multi-contrast diffusion MRI"}],"total_grants":4,"fwci":4.6074,"citation_percentile":0.96851306,"influential_citations":0,"citation_trend":[{"year":2024,"count":3},{"year":2025,"count":5},{"year":2026,"count":4}],"oa_status":"closed","license":"Springer Nature TDM","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-43993-3_40","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-3-031-43993-3_40","host_type":"book series"},{"url":"https://dx.doi.org/10.48550/arxiv.2307.09000","host_type":""},{"url":"http://arxiv.org/abs/2307.09000","host_type":""},{"url":"https://doi.org/10.48550/arXiv.2307.09000","host_type":""}],"fields_of_study":["Advanced Neuroimaging Techniques and Applications","Advanced MRI Techniques and Applications","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Tractography","Computer science","Artificial intelligence","Point cloud","Diffusion MRI","Pattern recognition (psychology)","Robustness (evolution)","Inference","Computer vision","Magnetic resonance imaging","FOS: Computer and information sciences","Computer Vision and Pattern Recognition (cs.CV)","Computer Science - Computer Vision and Pattern Recognition"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T15:52:49.592718Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}