AUTHOR=Bennett Alexis , Garner Rachael , Morris Michael D. , La Rocca Marianna , Barisano Giuseppe , Cua Ruskin , Loon Jordan , Alba Celina , Carbone Patrick , Gao Shawn , Pantoja Asenat , Khan Azrin , Nouaili Noor , Vespa Paul , Toga Arthur W. , Duncan Dominique TITLE=Manual lesion segmentations for traumatic brain injury characterization JOURNAL=Frontiers in Neuroimaging VOLUME=2 YEAR=2023 URL=https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2023.1068591 DOI=10.3389/fnimg.2023.1068591 ISSN=2813-1193 ABSTRACT=

Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis via inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.