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This study aimed to (1) optimize deep learning\u2010based brainstem segmentation for a wide range of pathologies and T1\u2010weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (<jats:italic>n<\/jats:italic>\u2009=\u2009257) was employed in the training of deep learning models based on multi\u2010dimensional gated recurrent units (MD\u2010GRU) or the nnU\u2010Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan\u2010rescan repeatability (<jats:italic>n<\/jats:italic>\u2009=\u200946) and inter\u2010scanner reproducibility (<jats:italic>n<\/jats:italic>\u2009=\u200920, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1\u2010year follow\u2010up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD\u2010GRU and nnU\u2010Net models demonstrated very good segmentation performance (median Dice coefficients \u2265\u20090.95 each) and outperformed a previously published model trained on a narrower dataset. Scan\u2013rescan repeatability and inter\u2010scanner reproducibility yielded similar Bland\u2013Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability\/reproducibility 0.68\/1.85), MD\u2010GRU (0.72\/1.46), and nnU\u2010Net (0.48\/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration.<\/jats:p>","DOI":"10.1002\/hbm.70141","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T10:20:01Z","timestamp":1739355601000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Extended Technical and Clinical Validation of Deep Learning\u2010Based Brainstem Segmentation for Application in Neurodegenerative Diseases"],"prefix":"10.1002","volume":"46","author":[{"given":"Benno","family":"Gesierich","sequence":"first","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Sander","sequence":"additional","affiliation":[{"name":"Neurologic Clinic and Policlinic, Departments of Neurology and Clinical Research University Hospital Basel and University of Basel  Basel Switzerland"},{"name":"Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering University of Basel  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Pirpamer","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominik S.","family":"Meier","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Ruberte","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"},{"name":"Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering University of Basel  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Amann","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Sinnecker","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Center (MIAC)  Basel Switzerland"},{"name":"Neurologic Clinic and Policlinic, Departments of Neurology and Clinical Research University Hospital Basel and University of Basel  Basel Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antal","family":"Huck","sequence":"additional","affiliation":[{"name":"Center for Medical Image Analysis &amp; 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