Deep Learning- Based Brain Ventricle Segmentation in Computed Tomography Using Domain Adaptation

Abstract

   Accurate segmentation of brain ventricles from CT scans is crucial for clinical procedures such as ventriculostomy, which involves draining excess fluid from the ventricles to control intracranial pressure. Ventriculostomy is often performed in acute settings, making CT imaging the most commonly available modality. Unlike MRI, there is a lack of publicly available, well-annotated databases for developing CT-based brain segmentation algorithms. Furthermore, there is a need for intuitive confidence measures for segmentation results produced by automated algorithms, such as deep learning methods, which can potentially improve the confidence and accuracy of clinical tasks. To address these needs, we propose an end-to-end uncertainty-aware domain adaptation technique for CT ventricle segmentation. This technique is based on the joint training of translation models and anatomical segmentation, leveraging unpaired MRI and CT scans without segmentation ground truths. For the translation task, we experimented with three different generative models: Cycle-Consistent Adversarial Networks (CycleGAN), Contrastive Learning for Unpaired Imageto-Image Translation (CUT) from GANs, and the Unpaired Neural SchrÅNodinger Bridge (UNSB) from diffusion models, and compared their results. For the segmentation phase, we employed an attention-based residual recurrent U-Net architecture to compare with U-Net and ResNet. Also, considering CycleGAN’s challenges with stability and structural consistency, we assessed various methods to understand their impact on translation and segmentation during our end-to-end training process. Additionally, we incorporated Monte Carlo dropouts in both MRI-to-CT translation and CT segmentation to provide an intuitive interpretation of the segmentation results.