Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4111-4114. doi: 10.1109/EMBC46164.2021.9631035.
In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions. The objective function is aimed to minimize Dice loss between the binary prediction and its actual labels. Samples having tumor information are considered for each patient data to avoid training on non-informative data. The models are trained on 222 HGG data and tested on 37 HGG data using performance metrics such as sensitivity, specificity, accuracy and Dice score. Test-time augmentation is also performed to improve the segmentation performance. 7-fold cross validation is conducted to analyze the performance on different sets of training and testing data.
本文针对脑肿瘤分割的流行 BraTS 数据集进行了研究。使用了 BraTS 2019 数据集，其中包含四种磁共振模态以及 259 例高级别胶质瘤 (HGG) 和 76 例低级别胶质瘤 (LGG) 患者数据的真实标签。我们针对每个正交平面（矢状面、冠状面和轴面）采用了基于 U-Net 架构的 2D 卷积神经网络 (CNN)，并融合了它们的预测结果。目标函数旨在最小化二进制预测与其实际标签之间的 Dice 损失。对于每个患者数据，仅考虑包含肿瘤信息的样本，以避免在无信息数据上进行训练。模型在 222 例 HGG 数据上进行训练，并在 37 例 HGG 数据上进行测试，使用灵敏度、特异性、准确性和 Dice 评分等性能指标进行评估。还进行了测试时扩充，以提高分割性能。通过 7 折交叉验证来分析在不同训练集和测试集上的性能。
