3D segmentation of uterine fibroids based on deep supervision and an attention gate.

Introduction: The segmentation of uterine fibroids is very important for the treatment of patients. However, uterine fibroids are small and have low contrast with surrounding tissue, making this task very challenging. To solve these problems, this paper proposes a 3D DA- VNet automatic segmentation method based on deep supervision and attention gate.

Methods: This method can accurately segment uterine fibroids in MRI images by convolutional information. We used 3DVnet as the underlying network structure and added a deep monitoring mechanism in the hidden layer. We introduce attention gates during the upsampling process to enhance focus on areas of interest. The network structure is composed of VNet, deep supervision module and attention gate module. The dataset contained 245 cases of uterine fibroids and was divided into a training set, a validation set, and a test set in a ratio of 6:2:2. A total of 147 patients' T2-weighted magnetic resonance (T2WI) images were used for training, 49 for validation, and 49 patients' MR Images were used for algorithm testing.

Results: Experimental results show that the proposed method achieves satisfactory segmentation results. Dice similarity coefficient (DSC), intersection ratio (IOU), sensitivity, precision and Hausdorff distance (HD) were 0.878, 0.784, 0.879, 0.885 and 11.180 mm, respectively.

Discussion: This shows that our proposed method can improve the automatic segmentation accuracy of magnetic resonance image (MRI) data of uterine fibroids to a certain extent.