深度学习用于多参数磁共振图像中前列腺癌的全自动检测、分割和Gleason分级估计
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images
摘要：
多参数磁共振成像（mpMRI）的出现对前列腺癌（PCa）的诊断产生了深远的影响，PCa是西方世界男性中最常见的恶性肿瘤，能够更好地选择患者进行确认活检。然而，分析这些图像即使对于专家来说也是复杂的，因此为计算机辅助诊断系统提供了机会。本文提出了一种基于深度学习的全自动系统，该系统从PCa疑似患者中获取前列腺mpMRI，并通过利用Retina U-Net检测框架，定位PCa病变，对其进行分割，并预测其最可能的Gleason分级组（GGG）。它使用490个mpMRI进行训练/验证，并使用75名患者进行测试，这些患者来自两个不同的数据集：ProstateX和IVO（瓦伦西亚肿瘤研究所基金会）。在测试集中，它实现了GGG $\geq$ 2显著性标准的极佳病变水平AUC/灵敏度/特异性，ProstateX数据集为0.96/1.00/0.79，IVO数据集为0.95/1.00/0.80。在患者水平上进行评价，ProstateX的结果为0.87/1.00/0.375，IVO为0.91/1.00/0.762。此外，在在线ProstateX大挑战中，该模型获得了0.85的AUC（仅在ProstateX数据上训练时为0.87，与挑战的原始获胜者联系在一起）。对于专家比较，IVO放射科医生的PI-RADS 4灵敏度/特异性在病变水平为0.88/0.56，在患者水平为0.85/0.58。还采用了用于自动前列腺分区分割和mpMRI非刚性序列配准的附加子系统来生成最终的全自动系统。ProstateX培训系统的代码已在https://github.com/OscarPellicer/prostate_lesion_detection上公开提供。我们希望这将成为未来研究的一个里程碑，以供使用，比较和改进。
英文摘要：
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates PCa lesions, segments them, and predicts their most likely Gleason grade group (GGG). It uses 490 mpMRIs for training/validation, and 75 patients for testing from two different datasets: ProstateX and IVO (Valencia Oncology Institute Foundation). In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG$\geq$2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. Evaluated at a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. Additional subsystems for automatic prostate zonal segmentation and mpMRI non-rigid sequence registration were also employed to produce the final fully automated system. The code for the ProstateX-trained system has been made openly available at https://github.com/OscarPellicer/prostate_lesion_detection. We hope that this will represent a landmark for future research to use, compare and improve upon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
