Academic Journal
Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability
| Title: | Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability |
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| Authors: | Reza Eghbali, Pierre Nedelec, David Weiss, Radhika Bhalerao, Long Xie, Jeffrey D. Rudie, Chunlei Liu, Leo P. Sugrue, Andreas M. Rauschecker |
| Source: | Neuroinformatics Neuroinformatics, vol 23, iss 1 |
| Publisher Information: | Springer Science and Business Media LLC, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Radiomics, Image Processing, Computer-Assisted/methods [MeSH], Neuroimaging/methods [MeSH], Brain/diagnostic imaging [MeSH], Research, Humans [MeSH], Software [MeSH], Image Interpretation, Computer-Assisted/methods [MeSH], Machine Learning [MeSH], Magnetic Resonance Imaging/methods [MeSH], Neuroradiology, MRI pipeline, Image Processing, Brain, Neuroimaging, Magnetic Resonance Imaging, Machine Learning, Computer-Assisted, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Humans, Image Interpretation, Software |
| Description: | 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. |
| Document Type: | Article Other literature type |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1559-0089 |
| DOI: | 10.1007/s12021-024-09708-z |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/39786657 https://repository.publisso.de/resource/frl:6492469 https://escholarship.org/content/qt9b68n6p4/qt9b68n6p4.pdf https://escholarship.org/uc/item/9b68n6p4 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....28ce845d8e244dc8da88239dd055dc5a |
| Database: | OpenAIRE |
| ISSN: | 15590089 |
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| DOI: | 10.1007/s12021-024-09708-z |