Evidence map›Paper›PMID 39786657›Full record

ArticleNeuroinformatics2025

Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability.

Reza Eghbali, Pierre Nedelec, David Weiss, Radhika Bhalerao, Long Xie, Jeffrey D Rudie, Chunlei Liu, Leo P Sugrue, Andreas M Rauschecker

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Reza EghbaliDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA. eghbali@berkeley.edu.ORCID https://orcid.org/0000-0003-4856-3059
Pierre NedelecDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0002-8535-1541
David WeissDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Radhika BhaleraoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0002-6020-7500
Long XieSiemens Healthineers, Erlangen, Germany.
Jeffrey D RudieDepartment of Radiology, University of California, San Diego, San Diego, CA, USA.
Chunlei LiuDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.ORCID https://orcid.org/0000-0001-8816-4832
Leo P SugrueDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Andreas M RauscheckerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingHumansImage Interpretation, Computer-AssistedMachine LearningNeuroimagingSoftwareMRI pipelineNeuroradiologyRadiomics

Identifiers

PMID39786657
PMCPMC11717894

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.