A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial areas of neurosurgery. We aim to develop a deep learning model for the automatic segmentation of intraparenchymal and intraventricular hemorrhage for clinical catheter puncture path planning. First, we develop a 3D U-Net embedded with a multi-scale boundary aware module and a consistency loss for segmenting two types of hematoma in computed tomography images. The multi-scale boundary aware module can improve the model's ability to understand the two types of hematoma boundaries. The consistency loss can reduce the probability of classifying a pixel into two categories at the same time. Since different hematoma volumes and locations have different treatments. We also measure hematoma volume, estimate centroid deviation, and compare with clinical methods. Finally, we plan the puncture path and conduct clinical validation. We collected a total of 351 cases, and the test set contained 103 cases. For intraparenchymal hematomas, the accuracy can reach 96$ \% $ when the proposed method is applied for path planning. For intraventricular hematomas, the proposed model's segmentation efficiency and centroid prediction are superior to other comparable models. Experimental results and clinical practice show that the proposed model has potential for clinical application. In addition, our proposed method has no complicated modules and improves efficiency, with generalization ability.