Segmentation of Intracerebral Hemorrhage based on Improved U-Net

Automatic medical image segmentation effectively aids in stroke diagnosis and treatment. In this article, an improved U-Net neural network for auxiliary diagnosis of intracerebral hemorrhage is proposed, which can realize the automatic segmentation of hemorrhage from brain CT images. The pixels of brain CT images are first clustered into four classes: gray matter, white matter, cerebrospinal fluid, and hemorrhage by fuzzy c-means (FCM) clustering, followed by the removal of the skull by morphological imaging, and finally an improved U-Net neural network model is proposed to automatically segment hemorrhages from the brain CT images. Experiment results showed that the objective function of binary cross-entropy was better than dice loss and focal loss for the proposed method. Its dice similarity coefficient reached 0.860 ± 0.031, which was better than the methods of white matter FCM clustering and multipath context generation adversarial networking. This improved method dramatically enhanced the accuracy of segmentation for intracerebral hemorrhage.