论文标题
总体生存预测的3D语义分割
3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction
论文作者
论文摘要
神经瘤是恶性脑肿瘤，需要立即治疗以改善患者的存活率。胶质瘤异质性使分割变得困难，尤其是对于坏死，增强肿瘤，非增强肿瘤和水肿等子区域。深层神经网络，例如全卷积神经网络和完全卷积神经网络的整体，成功进行神经胶质瘤分割。本文证明了使用3D完全卷积神经网络的使用，该网络具有三层编码器解码器方法进行层布置。编码器块包括密集模块，解码器块包括卷积模块。网络的输入是3D补丁。损耗函数结合了骰子损失和局灶性损失函数。对于增强肿瘤，整个肿瘤和肿瘤核心的验证集骰子分别为0.74、0.88和0.73。随机森林回归者使用从地面真相提取的形状，体积和年龄特征来进行总体生存预测。回归器在验证集上的准确度为44.8％。
Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumor, non-enhancing tumor, and Edema. Deep neural networks like full convolution neural networks and ensemble of fully convolution neural networks are successful for Glioma segmentation. The paper demonstrates the use of a 3D fully convolution neural network with a three layer encoder decoder approach for layer arrangement. The encoder blocks include the dense modules, and decoder blocks include convolution modules. The input to the network is 3D patches. The loss function combines dice loss and focal loss functions. The validation set dice score of the network is 0.74, 0.88, and 0.73 for enhancing tumor, whole tumor, and tumor core, respectively. The Random Forest Regressor uses shape, volumetric, and age features extracted from ground truth for overall survival prediction. The regressor achieves an accuracy of 44.8% on the validation set.
