Purpose: Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT.
Methods: We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations.
Results: The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data.
Conclusion: This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.
Keywords: Acute ischemic stroke; Deep learning; Image segmentation; Non-contrast computed tomography.
© 2022. CARS.