Brain segmentation in patients with perinatal arterial ischemic stroke

•Deep learning allows automatic segmentation of brain tissues and ischemic lesions in MRI scans of patients affected by perinatal arterial stroke.•Qualitative evaluation of the automatic segmentations showed that in 85% of MRIs acquired after the stroke onset and in 91% of MRIs acquired at 3-moths follow-up were clinically useable after no or minor correction.•Automatic segmentation method may allow evaluation of the brain development and efficacy of stroke treatment in large scale studies. Perinatal arterial ischemic stroke (PAIS) is associated with adverse neurological outcomes. Quantification of ischemic lesions and consequent brain development in newborn infants relies on labor-intensive manual assessment of brain tissues and ischemic lesions. Hence, we propose an automatic method utilizing convolutional neural networks (CNNs) to segment brain tissues and ischemic lesions in MRI scans of infants suffering from PAIS. This single-center retrospective study included 115 patients with PAIS that underwent MRI after the stroke onset (baseline) and after three months (follow-up). Nine baseline and 12 follow-up MRI scans were manually annotated to provide reference segmentations (white matter, gray matter, basal ganglia and thalami, brainstem, ventricles, extra-ventricular cerebrospinal fluid, and cerebellum, and additionally on the baseline scans the ischemic lesions). Two CNNs were trained to perform automatic segmentation on the baseline and follow-up MRIs, respectively. Automatic segmentations were quantitatively evaluated using the Dice coefficient (DC) and the mean surface distance (MSD). Volumetric agreement between segmentations that were manually and automatically obtained was computed. Moreover, the scan quality and automatic segmentations were qualitatively evaluated in a larger set of MRIs without manual annotation by two experts. In addition, the scan quality was qualitatively evaluated in these scans to establish its impact on the automatic segmentation performance. Automatic brain tissue segmentation led to a DC and MSD between 0.78–0.92 and 0.18–1.08 mm for baseline, and between 0.88–0.95 and 0.10–0.58 mm for follow-up scans, respectively. For the ischemic lesions at baseline the DC and MSD were between 0.72–0.86 and 1.23–2.18 mm, respectively. Volumetric measurements indicated limited oversegmentation of the extra-ventricular cerebrospinal fluid in both the follow-up and baseline scans, oversegmentation of the ischemic lesions in the left hemi