Toward Automated Detection of Silent Cerebral Infarcts in Children and Young Adults With Sickle Cell Anemia

Abstract

Background: Silent cerebral infarcts (SCI) in sickle cell anemia (SCA) are associated with future strokes and cognitive impairment, warranting early diagnosis and treatment. Detection of SCI, however, is limited by their small size, especially when neuroradiologists are unavailable. We hypothesized that deep learning may permit automated SCI detection in children and young adults with SCA as a tool to identify the presence and extent of SCI in clinical and research settings.

Methods: We utilized UNet-a deep learning model-for fully automated SCI segmentation. We trained and optimized UNet using brain magnetic resonance imaging from the SIT trial (Silent Infarct Transfusion). Neuroradiologists provided the ground truth for SCI diagnosis, while a vascular neurologist manually delineated SCI on fluid-attenuated inversion recovery and provided the ground truth for SCI segmentation. UNet was optimized for the highest spatial overlap between automatic and manual delineation (dice similarity coefficient). The optimized UNet was externally validated using an independent single-center prospective cohort of SCA participants. Model performance was evaluated through sensitivity and accuracy (%correct cases) for SCI diagnosis, dice similarity coefficient, intraclass correlation coefficient (metric of volumetric agreement), and Spearman correlation.

Results: The SIT trial (n=926; 31% with SCI; median age, 8.9 years) and external validation (n=80; 50% with SCI; age, 11.5 years) cohorts had small median lesion volumes of 0.40 and 0.25 mL, respectively. Compared with the neuroradiology diagnosis, UNet predicted SCI presence with 100% sensitivity and 74% accuracy. In magnetic resonance imaging with SCI, UNet reached a moderate spatial agreement (dice similarity coefficient, 0.48) and high volumetric agreement (intraclass correlation coefficient, 0.76; ρ=0.72; P<0.001) between automatic and manual segmentations.

Conclusions: UNet, trained using a large pediatric SCA magnetic resonance imaging data set, sensitively detected small SCI in children and young adults with SCA. While additional training is needed, UNet may be integrated into the clinical workflow as a screening tool, aiding in SCI diagnosis.

Keywords: cerebral infarct; deep learning; diagnostic imaging; sickle cell anemia; white matter diseases.

Figure 1.. Schematic of UNet architecture.

The UNet is a deep learning network that is increasingly utilized for automatic medical image segmentation (A). It is comprised of a contracting path (green boxes), an expanding path (blue boxes), and a bridge that connects the two paths (orange box). The contracting path is a feature extractor and learns an abstract representation of the input 2D axial FLAIR image through a sequence of the contraction layers. Four layers are depicted in the figure. Within each layer, UNet goes through convolutions, which are linear operations that multiplies a set of image pixel values to two-dimensional weights (filter, B). Repeated overlapping application of this filter creates a two-dimensional output array known as a feature map. Each value in the feature map is then passed through a nonlinearity activation function to improve network learning. Finally, the spatial dimensions (height and width) of the feature maps are reduced by half between each contraction layers to reduce the computational time by decreasing the number of trainable parameters. The expanding path projects the abstract representation of the image in the form of lower resolution discriminative, abstract features learnt during the contracting path, onto the higher resolution voxel space to eventually generate an output segmentation mask. This is carried out within the expansion layers through upsampling which restores the condensed feature maps to the original size of the input image by expanding the feature dimensions, and combining with skip connection (gray arrow) feature maps from the corresponding expansion layers. These skip connections ensure conservation of features from earlier layers to preserve learning. This design allows the UNet to learn increasingly complex features at multiple scales without sacrificing spatial information. Finally, the feature maps are converted to an output probability map indicating the probability of each image voxel being a SCI, with spatial dimensions corresponding to the original FLAIR image. The UNet-estimated SCI volume was calculated as the product of [the number of SCI positive voxels with a probability >=0.5] and [the volume of each voxel].

Figure 2.. Representative UNet segmentation of SCI.

The original FLAIR map, manual segmentation mask, and UNet segmentation mask are shown. To determine spatial agreement, the manual segmentation was overlaid onto the UNet segmentation (combined). A region containing SCI, before and after segmentations, was enlarged for better visualization (boxes). Manual segmentation was defined as the “ground truth”. False negative, false positive, and accurate segmentations are shown in green, red, and yellow, respectively. In this example, UNet achieved a Dice Similarity Coefficient of 0.48.

Figure 3.. SCI misclassification by UNet.

For demonstration purposes, false negative SCI case examples were selected from the optimization dataset as no false negatives were present in the external validation cohort. In the optimization dataset, UNet failed to segment small and faint SCI lesions in the subcortical white matter (arrow, A). In false positive SCI case examples from the external validation cohort, UNet erroneously segmented portions of cortical gray matter (arrow, B) and CSF pulsation artifact adjacent to the lateral ventricle (arrow, C) as SCI lesions. UNet also segmented white matter FLAIR hyperintensities smaller than 3mm in diameter (arrow, D), which fell below the size threshold for radiological definition of SCI, and symmetrical periventricular white matter changes (arrow, E) that are non-specific in adults and of unknown significance in children.

Figure 4.. UNet SCI volume estimation in optimization and external validation cohort.

Simple correlations between UNet volume vs ground-truth volume in the optimization dataset (A) and external validation cohort (B) are shown. UNet volume was strongly and highly correlated with ground-truth volume. Linear regression line (solid line) with 95% confidence interval (dashed lines) are shown. On Bland-Altman plots, in the optimization dataset, there was a proportional bias where UNet systematically underestimated SCI volume (p < 0.001, C). On average, UNet underestimated SCI volume by 0.86 mL, trending significance (p = 0.08, C). In contrast, in the external validation cohort, there was no proportional bias in UNet predicted volumes compared to ground-truth volumes (p = 0.34, D). Its averaged difference between UNet and ground-truth volumes was not statistically different from zero (mean bias 0.13 mL, p = 0.11). The mean bias (solid line) and the lower and upper limits of agreement (LOA, ±1.96 *standard deviation, dashed lines) are shown. A negative difference on the Y-axis represents under-estimation by machine learning. A positive difference represents over-estimation by machine learning. *One Sample t-Test. † Linear regression.