论文标题
使用深证网络从3D PET-CT图像分割的淋巴瘤分割
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
论文作者
论文摘要
提出了一种基于Dempster-Shafer理论和深度学习的自动证据分割方法，以从三维正电子发射断层扫描（PET）和计算机断层扫描（CT）图像中分割淋巴瘤。该体系结构由深度功能萃取模块和证据层组成。功能提取模块使用编码器框架框架从3D输入中提取语义特征向量。然后，证据层在特征空间中使用原型来计算每个体素的信念函数，以量化有关该位置存在或不存在淋巴瘤的不确定性。基于使用距离的不同方式，比较了两个证据层，以计算质量函数。通过最小化骰子损失函数，对整个模型进行了训练。表明，在173名患者的数据集中，提出的深度提取和证据分割的组合表现出优于基线UNET模型以及其他三个最先进的模型。
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The feature extraction module uses an encoder-decoder framework to extract semantic feature vectors from 3D inputs. The evidential layer then uses prototypes in the feature space to compute a belief function at each voxel quantifying the uncertainty about the presence or absence of a lymphoma at this location. Two evidential layers are compared, based on different ways of using distances to prototypes for computing mass functions. The whole model is trained end-to-end by minimizing the Dice loss function. The proposed combination of deep feature extraction and evidential segmentation is shown to outperform the baseline UNet model as well as three other state-of-the-art models on a dataset of 173 patients.
