The School of Engineering and Technology, Fudan University, Shanghai, 200433, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China.
Comput Biol Med. 2022 Dec;151(Pt A):106279. doi: 10.1016/j.compbiomed.2022.106279. Epub 2022 Nov 9.
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.
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.
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.
脑膜瘤的治疗通常包括手术切除、放射治疗和化学治疗。肿瘤的准确分割显著促进了完全手术切除和精确放射治疗，从而提高了患者的生存率。本文构建了一种基于磁共振 T1 加权对比增强（T1CE）图像的深度学习模型，以开发用于准确肿瘤分割的自动处理方案。
本文提出了一种新的卷积神经网络（CNN）模型，用于准确分割磁共振图像中的脑膜瘤。它可以根据脑膜瘤的磁共振图像特征，在同一特征图的多尺度感受野中提取融合特征。注意力机制被添加到模型中作为一个有用的补充，以优化特征信息的传输。
在两个内部测试集和一个外部测试集上进行了评估。分别得到了 0.886、0.851 和 0.874 的平均 Dice 相似系数（DSC）值。本文提出了一种基于深度学习的方法来分割 T1CE 图像中的肿瘤。多中心测试集验证了该方法的有效性和泛化能力。所提出的模型展示了肿瘤分割的最新性能。
