论文标题
T2加权3D快速自旋回波图像中前列腺和外围区的全自动分割的级联剩余UNET
A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images
论文作者
论文摘要
多参数MR图像已被证明对前列腺癌的非侵入性诊断有效。前列腺的自动分割消除了耗时的放射科医生的手动注释的需求。这提高了成像特征提出前列腺组织的效率。在这项工作中，我们提出了一个完全自动化的深度学习架构，其中有残留的块，级联的MRES-UNET，以一次通过网络对前列腺和外围区域进行分割。与经验丰富的放射科医生的手动注释相比，该网络具有高骰子分数（$ 0.91 \ pm.02 $），精度（0.91 \ pm.04 $），并在前列腺分段中进行召回分数（$ 0.92 \ pm.03 $）。总前列腺量估计的平均差异小于5％。
Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual annotation by a radiologist which is time consuming. This improves efficiency in the extraction of imaging features for the characterization of prostate tissues. In this work, we propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone in one pass through the network. The network yields high Dice scores ($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in prostate segmentation compared to manual annotations by an experienced radiologist. The average difference in total prostate volume estimation is less than 5%.
