A Deep Learning Segmentation Method for Analyzing Intraventricular Hemorrhage Secondary to Hypertension

Background and Objective: When hypertensive cerebral hemorrhage causes secondary intraventricular hemorrhage, it usually significantly increases the complexity of the patient’s condition and the risk of poor prognosis. Deep learning methods can automatically and quickly segment intraparenchymal hemorrhage and intraventricular hemorrhage, and quantitatively analyze hematoma-related properties to provide auxiliary information for subsequent diagnosis and treatment. Methods: We retrospectively enrolled 351 patients with hypertensive cerebral hemorrhage, 219 of whom had secondary intraventricular hemorrhage. All patients underwent computed tomography within 1 week after diagnosis. Based on 3D U-Net, we developed a deep learning network with a multi-scale deformable convolution module and a softened anatomical consistency loss. The multi-scale deformable convolution module can enhance the learning ability of multi-deformation features and increase the receptive field of the network. The anatomical consistency loss, built upon softened labels, can alleviate the impact of label noise. Results: We evaluated our model at pixel, volume, and morphology levels. It achieved Dice of 0.8990 ± 0.1169 for intraparenchymal hemorrhage and 0.7124 ± 0.1227 for intraventricular hemorrhage, both higher than that of the comparison model. Compared with the Coniglobus method, our model has a narrower consistency limit and more concentrated predicted values. Additionally, in segmenting irregular and different-sized hematomas, it generates the smallest centroid, volume, position, and morphology deviations. Conclusions: The proposed model can automatically and accurately segment two types of hematomas and quantify multiple attributes, is robust in multi-deformation and label noise scenarios, and has the potential to assist in clinical diagnosis and treatment; its actual impact on clinical decision-making and patient outcomes requires prospective validation.