MRI是诊断前列腺癌的主要影像学方法。多参数MRI(mpMRI)上的前列腺成像报告和数据系统(PI-RADS)提供了基本的MRI解释指南,但存在读者之间的差异。深度学习网络在自动病变分割和分类方面显示出巨大的前景,这有助于减轻放射科医师的负担并减少读者之间的差异。在这项研究中,我们提出了一种新颖的多分支网络,MiniSegCaps,用于MPMRI上的前列腺癌分割和PI-RADS分类。MiniSeg分支结合PI-RADS预测输出分割,由CapsuleNet的注意力地图引导。CapsuleNet分支利用前列腺癌的相对空间信息到解剖结构,例如病变的区域位置,由于其等方差特性,这也降低了训练中的样本量要求。此外,采用门控递归单元(GRU)来利用跨切片的空间知识,提高通过平面的一致性。根据临床报告,我们从462例患者中建立了前列腺mpMRI数据库,并结合放射学估计的注释.通过五次交叉验证对MiniSegCaps进行了训练和评估。在93个测试案例中,我们的模型在病变分割上实现了0.712的骰子系数,准确率为89.18%,在患者水平评估中,PI-RADS分类(PI-RADS≥4)的敏感性为92.52%,显著优于现有方法。此外,集成到临床工作流程中的图形用户界面(GUI)可以根据MiniSegCaps的结果自动生成诊断报告。
MRI is the primary imaging approach for diagnosing prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability. In this study, we proposed a novel multi-branch network, MiniSegCaps, for prostate cancer segmentation and PI-RADS classification on mpMRI. MiniSeg branch outputted the segmentation in conjunction with PI-RADS prediction, guided by the attention map from the CapsuleNet. CapsuleNet branch exploited the relative spatial information of prostate cancer to anatomical structures, such as the zonal location of the lesion, which also reduced the sample size requirement in training due to its equivariance properties. In addition, a gated recurrent unit (GRU) is adopted to exploit spatial knowledge across slices, improving through-plane consistency. Based on the clinical reports, we established a prostate mpMRI database from 462 patients paired with radiologically estimated annotations. MiniSegCaps was trained and evaluated with fivefold cross-validation. On 93 testing cases, our model achieved a 0.712 dice coefficient on lesion segmentation, 89.18% accuracy, and 92.52% sensitivity on PI-RADS classification (PI-RADS ≥ 4) in patient-level evaluation, significantly outperforming existing methods. In addition, a graphical user interface (GUI) integrated into the clinical workflow can automatically produce diagnosis reports based on the results from MiniSegCaps.