论文标题
在MP-MRI的深奥前列腺癌检测中进行灌注成像：我们可以利用它吗？
Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?
论文作者
论文摘要
据我们所知，所有用于前列腺癌（PCA）检测的深度计算机辅助检测和诊断（CAD）系统仅考虑双参数磁共振成像（BP-MRI），包括T2W和ADC序列，同时不包括4D灌注序列，但是这是此诊断任务的标准临床方案的一部分。在本文中，我们质疑策略以将深度神经体系结构中的灌注成像中的信息整合在一起。为此，我们评估了几种方法来在U-NET等U-NET中编码灌注信息，也考虑了早期融合策略和中期融合策略。我们将多参数MRI（MP-MRI）模型的性能与基于219 MP-MRI考试的专用数据集的基线BP-MRI模型进行了比较。从动态对比度增强的MR考试得出的灌注图显示出对PCA病变的分割和分级性能的积极影响，尤其是对应于洗涤曲线的最大斜率以及TMAX灌注图的3D MR体积。无论融合策略如何，后者的MP-MRI模型确实优于BP-MRI，Cohen的Kappa得分为0.318 $ \ pm $ 0.019，BP-MRI型号和0.378 $ \ pm $ 0.033，该型号的最大坡度和中间融合策略的最大斜坡，以及具有富有融合的COHEN COHEN COHEN'S COHEN'S ARTA的最大斜坡。
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategies. We compare performance of multiparametric MRI (mp-MRI) models with the baseline bp-MRI model based on a private dataset of 219 mp-MRI exams. Perfusion maps derived from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions, especially the 3D MR volume corresponding to the maximum slope of the wash-in curve as well as Tmax perfusion maps. The latter mp-MRI models indeed outperform the bp-MRI one whatever the fusion strategy, with Cohen's kappa score of 0.318$\pm$0.019 for the bp-MRI model and 0.378 $\pm$ 0.033 for the model including the maximum slope with a mid fusion strategy, also achieving competitive Cohen's kappa score compared to state of the art.
