A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation - Nijiati, Mayidili; Tuersun, Abudouresuli; Zhang, Yue; Yuan, Qing; Gong, Ping; Abulizi, Abudoukeyoumujiang; Tuoheti, Awanisa; Abulaiti, Adili; Zou, Xiaoguang (Frontiers in Physiology, 2022)
Abstract Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1157 patients diagnosed with ICH is established to train (n=857), validate (n=100), and test (n=200) the Sym-TransNet. An healthy cohort of 200 subjects is added, establishing a test set with balanced postive and negtive cases (n=400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare against Sym-TransNet with statistical analyses. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the segmentation accuracy of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716±0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784±0.039, 0.680±0.049, 0.359±0.186, 0.534±0.455, and 0.337±0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the segmentation accuracy of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusions: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.