A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images. (December 2022) - Ma, Xin; Zhao, Yajing; Lu, Yiping; Li, Peng; Li, Xuanxuan; Mei, Nan; Wang, Jiajun; Geng, Daoying; Zhao, Lingxiao; Yin, Bo
Abstract: Background and objective: Treatment for meningiomas usually includes surgical removal, radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly facilitates complete surgical resection and precise radiotherapy, thereby improving patient survival. In this paper, a deep learning model is constructed for magnetic resonance T1-weighted Contrast Enhancement (T1CE) images to develop an automatic processing scheme for accurate tumor segmentation. Methods: In this paper, a novel Convolutional Neural Network (CNN) model is proposed for the accurate meningioma segmentation in MR images. It can extract fused features in multi-scale receptive fields of the same feature map based on MR image characteristics of meningiomas. The attention mechanism is added as a helpful addition to the model to optimize the feature information transmission. Results and conclusions: The results were evaluated on two internal testing sets and one external testing set. Mean Dice Similarity Coefficient (DSC) values of 0.886, 0.851, and 0.874 are demonstrated, respectively. In this paper, a deep learning approach is proposed to segment tumors in T1CE images. Multi-center testing sets validated the effectiveness and generalization of the method. The proposed model demonstrates state-of-the-art tumor segmentation performance. Highlights: A novel dual-branch V-shaped CNN model is proposed for meningioma segmentation. The HDC block has been improved for efficient extraction of global features. Multi-center data from 551 patients were used in our study for training and testing.