The delineation of largely deformed brain midline using regression-based line detection network.

PURPOSE The human brain has two cerebral hemispheres that are roughly symmetric and separated by a midline, which is nearly a straight line shown in axial CT images in healthy subjects. However, brain diseases such as hematoma and tumors often cause midline shift, where the degree of shift can be regarded as a quantitative indication in clinical practice. To facilitate clinical evaluation, we need computer-aided methods to automate this quantification. Nevertheless, most existing studies focused on the landmark- or symmetry-based methods that provide only the existence of shift or its maximum distance, which could be easily affected by anatomical variability and large brain deformations. Intuitive results such as midline delineation or measurement are lacking. In this study, we focus on developing an automated and robust method based on the fully convolutional neural network for the delineation of midline in largely deformed brains. METHODS We propose a novel regression-based line detection network (RLDN) for the robust midline delineation, especially in largely deformed brains. Specifically, to improve the robustness of delineation in largely deformed brains, we regard the delineation of the midline as the skeleton extraction task and then use the multi-scale bidirectional integration module to acquire more representative features. Based on the skeleton extraction, we incorporate the regression task into it to delineate more accurate and continuous midline, especially in largely deformed brains. Our study utilized the public CQ 500 dataset (128 subjects) for training with hold-out validation on 61 subjects from a private cohort accrued from a local hospital. RESULTS The mean line distance error and F1-score were 1.17 ± 0.72 mm with 0.78 on CQ 500 test set, and 4.15 ± 3.97 mm with 0.61 on the private dataset. Besides, significant differences (p < 0.05) were observed between our method and other comparative ones on these two datasets. CONCLUSIONS This work provides a novel solution to acquire robust delineation of the midline, especially in largely deformed brains, and achieves state-of-the-art performance on the public and our private dataset, which makes it possible for automated diagnosis of relevant brain diseases in the future.