A standardized protocol for manually segmenting stroke lesions on high-resolution T1-weighted MR images

Bethany P Lo; Miranda R Donnelly; Giuseppe Barisano; Sook-Lei Liew

doi:10.3389/fnimg.2022.1098604

A standardized protocol for manually segmenting stroke lesions on high-resolution T1-weighted MR images

Front Neuroimaging. 2023 Jan 10:1:1098604. doi: 10.3389/fnimg.2022.1098604. eCollection 2022.

Authors

Bethany P Lo¹, Miranda R Donnelly¹, Giuseppe Barisano², Sook-Lei Liew^{1

3}

Affiliations

¹ Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
² Department of Neurosurgery, Stanford University, Stanford, CA, United States.
³ Keck School of Medicine, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.

Abstract

Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions ("tracers") in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.

Keywords: ITK-SNAP; MRI segmentation; T1w MRI; manual segmentation; segmentation protocol; stroke; stroke lesion.

Abstract

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