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Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data\u2010driven approaches including deep\u2010learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and\/or computationally intensive image processing at the point of application. We describe an atlas\u2010based direct tract mapping technique called \u2018tractfinder\u2019, utilising tract\u2010specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10\u201320 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro\u2010anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel\u2010wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep\u2010learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3\u2009min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.<\/jats:p>","DOI":"10.1002\/hbm.26578","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T14:56:39Z","timestamp":1706626599000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fibre orientation atlas guided rapid segmentation of white matter tracts"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6803-0690","authenticated-orcid":false,"given":"Fiona","family":"Young","sequence":"first","affiliation":[{"name":"Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health University College London  London UK"},{"name":"Department of Medical Physics and Biomedical Engineering University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7015-5196","authenticated-orcid":false,"given":"Kristian","family":"Aquilina","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Great Ormond Street Hospital for Children  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-7716","authenticated-orcid":false,"given":"Kiran K.","family":"Seunarine","sequence":"additional","affiliation":[{"name":"Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health University College London  London UK"},{"name":"Department of Radiology Great Ormond Street Hospital for Children  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4596-3551","authenticated-orcid":false,"given":"Laura","family":"Mancini","sequence":"additional","affiliation":[{"name":"Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London Hospitals NHS Foundation Trust  London UK"},{"name":"Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8237-6065","authenticated-orcid":false,"given":"Chris A.","family":"Clark","sequence":"additional","affiliation":[{"name":"Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6608-0619","authenticated-orcid":false,"given":"Jonathan D.","family":"Clayden","sequence":"additional","affiliation":[{"name":"Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.10.019"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0231440"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.06.020"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-022-02736-7"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10609"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.09.018"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117402"},{"key":"e_1_2_11_10_1","unstructured":"Brainlab. 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