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An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies
Journal article   Open access

An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies

Sarthak Pati, Stefan Wagner, Siddhesh Thakur, Evan Calabrese, Russell Shinohara and Spyridon Bakas
Journal of imaging informatics in medicine, v 39(2), pp 1608-1618
01 Apr 2026
DOI: https://doi.org/10.1007/s10278-025-01570-y
PMID: 40563038
url
https://doi.org/10.1007/s10278-025-01570-yView
Published, Version of Record (VoR) Open CC BY V4.0

Abstract

Original Paper
Brain extraction is essential in neuroimaging studies for patient privacy and optimizing computational analyses. Manual creation of 3D brain masks is labor-intensive, prompting the development of automatic computational methods. Robust quality control (QC) is hence necessary for the effective use of these methods in large-scale studies. However, previous automated QC methods have been limited in flexibility regarding algorithmic architecture and data adaptability. We introduce a novel approach inspired by a statistical outlier detection paradigm to efficiently identify potentially erroneous data. Our QC method is unsupervised, resource-efficient, and requires minimal parameter tuning. We quantitatively evaluated its performance using morphological features of brain masks generated from three automated brain extraction tools across multi-institutional pre- and post-operative brain glioblastoma MRI scans. We achieved an accuracy of 0.9 for pre- and 0.87 for post-operative scans, thus demonstrating the effectiveness of our proposed QC tool for brain extraction. Additionally, the method shows potential for other tasks where a user-defined feature space can be defined. Our novel QC approach offers significant improvements in flexibility and efficiency over previous methods. It is a valuable tool, targeting reassurance of brain masks in neuroimaging and can be adapted for other applications requiring robust QC mechanisms.

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