Deformable midline shift network: automatic brain-midline shift detection using dynamic snake convolution

The brain midline is a virtual boundary between the left and right hemispheres, usually straight under normal conditions due to the dynamic constancy of intracranial contents. However, conditions such as hemorrhagic stroke can cause a mass effect due to hematoma and edema, causing Midline Shift (MLS). MLS is crucial for assessing the severity of hemorrhagic stroke. Traditionally, midline identification is manually performed by clinicians, which is time-consuming and prone to inconsistencies. In recent years, Computer-Aided Diagnosis (CAD) methods have emerged to automate this process, improving efficiency and accuracy. These CAD approaches generally fall into two categories: symmetry-based and landmark-based methods. While these methods have contributed to MLS detection, they have limitations in capturing the elongated structure of the midline and providing continuous predictions. In this study, we propose a novel method for brain MLS estimation. First, we propose an ellipse-based correction method to align CT images, ensuring direction consistency. Importantly, we propose Deformable Midline Shift Network (DMSNet), which utilizes Dynamic Snake Convolution (DSConv) to better extract the elongated structural features of the brain midline. The model's regression-based approach allows for continuous and smooth midline predictions, improving accuracy. The DMSNet outperforms traditional convolutional networks and state-of-the-art models, achieving superior results in multiple evaluation metrics. Moreover, the model demonstrates high accuracy in assessing surgical indications based on MLS, further proving its clinical relevance.