Study of multistep Dense U‐Net‐based automatic segmentation for head MRI scans

Abstract Background Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. Purpose The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U‐Net (MDU‐Net) architecture. Methods The MDU‐Net‐based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U‐Net and 3D Dense U‐Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U‐Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U‐Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U‐Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS‐3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res‐U‐Net, Dense U‐Net, U‐Net++, Swin‐Unet, and H‐Dense U‐Net models. Results The MDU‐Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU‐Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H‐Dense U‐Net model, which showed the highest performance among the other models, the MDU‐Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU‐Net model outperformed the H‐Dense U‐Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. Conclusion Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU‐Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.