Semantic Segmentation of Intracranial Hemorrhages in Head CT Scans

This paper presents a semantic segmentation method that can distinguish six different types of intracranial hemorrhage and calculate the amount of blood loss. The major challenge of medical image segmentation are the lack of enough data due to the difficulty of data collection and labeling. In this paper, we propose to adopt a pretrained U-Net model with fine tuning to solve this problem. The best final test accuracy can reach 94.1%, which is 10.5% higher than the model training from scratch, proving its advantages in dealing with relatively complex datasets with a small amount of data, and the success of the proposed segmentation method.