论文标题
前列腺网络：通过MRI扫描中的侵略性进行前列腺癌分割的深刻注意模型
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
论文作者
论文摘要
多参数磁共振成像（MP-MRI）在检测前列腺癌（PCA）方面表现出了很好的结果。但是，在临床实践中表征前列腺病变侵袭性是不可能的，而活检仍然是确定格里森评分（GS）的参考。在这项工作中，我们提出了一个新型的端到端多级网络，该网络共同将前列腺和癌症病变与GS组分级共同分割。编码潜在空间上的信息后，网络分为两个分支：1）第一个分支执行前列腺分割2）第二个分支使用此区域先验作为注意门检测和分级前列腺病变。该模型在前列腺切除术之前在三种不同的扫描仪上获得的219次MRI考试进行了5倍的交叉验证训练和验证。在临床上重要病变（定义为GS> 6）检测的自由响应接收器工作特性（FROC）分析中，我们的模型在整个前列腺上的每位患者的敏感性为69.0％$ \ pm $ 14.5％，在1.5％$ \ pm $ 14.4％的敏感性时，仅在1.5 peripheral时（PPE）peripheral时（pz）（pz）（PZ）（PP）。关于自动GS组
Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical practice, and biopsy remains the reference to determine the Gleason score (GS). In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs prostate segmentation 2) the second branch uses this zonal prior as an attention gate for the detection and grading of prostate lesions. The model was trained and validated with a 5-fold cross-validation on an heterogeneous series of 219 MRI exams acquired on three different scanners prior prostatectomy. In the free-response receiver operating characteristics (FROC) analysis for clinically significant lesions (defined as GS > 6) detection, our model achieves 69.0% $\pm$14.5% sensitivity at 2.9 false positive per patient on the whole prostate and 70.8% $\pm$14.4% sensitivity at 1.5 false positive when considering the peripheral zone (PZ) only. Regarding the automatic GS group
