Abstract
Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp-MRI can be subjective, development of computer-aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask-RCNN model to segment the prostate and ILs. We trained and evaluated models on 120 patients from two different cohorts of patients. We also used 2D U-Net and 3D U-Net as benchmarks to segment the prostate and compared the model's performance. The contour variability of ILs using the algorithm was also benchmarked against the interobserver variability between two different radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN model is able to reach state-of-art performance in the prostate segmentation and outperforms several competitive baselines in ILs segmentation.
Abstract (translated)
前列腺癌（PCA）是美国男性最常见的癌症。多参数磁共振成像（MP-MRI）已被许多研究者探索，以前列腺活检和放射治疗为目标。然而，核磁共振成像的评估可能是主观的，开发计算机辅助诊断系统来自动描绘前列腺和前列腺内病变（ILS）在临床实践中对于放射科医生来说是非常重要的。本文首先研究了面膜RCNN模型在前列腺和ILS分割中的应用。我们对来自两组不同患者的120名患者进行了模型培训和评估。我们还使用了2d u-net和3d u-net作为基准来分割前列腺，并比较了模型的性能。使用该算法计算的ILS轮廓变异性也以19名患者的两名不同放射肿瘤学家的观察者间变异性为基准。我们的研究结果表明，mask-rcnn模型在前列腺分割中能够达到最先进的性能，并且在ILS分割中优于几个竞争基线。
URL
https://arxiv.org/abs/1904.02575
