{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:42:06Z","timestamp":1774719726359,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Innovate for Health Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline. In this manuscript, we give an overview of ALFE, present the main aspects of ALFE pipeline design philosophy, and present case studies.<\/jats:p>","DOI":"10.1007\/s12021-024-09708-z","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T05:55:36Z","timestamp":1736402136000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4856-3059","authenticated-orcid":false,"given":"Reza","family":"Eghbali","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8535-1541","authenticated-orcid":false,"given":"Pierre","family":"Nedelec","sequence":"additional","affiliation":[]},{"given":"David","family":"Weiss","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6020-7500","authenticated-orcid":false,"given":"Radhika","family":"Bhalerao","sequence":"additional","affiliation":[]},{"given":"Long","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Jeffrey D.","family":"Rudie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8816-4832","authenticated-orcid":false,"given":"Chunlei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Leo P.","family":"Sugrue","sequence":"additional","affiliation":[]},{"given":"Andreas M.","family":"Rauschecker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"9708_CR1","doi-asserted-by":"crossref","unstructured":"Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. 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The Institution name Berkeley was incorrectly written as Berekely and the ORCID ID of the fourth author, Radhika Bhalerao: 0000-0002-6020-7500 was added.","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}