Cem Birbiri Ufuk, Hamidinekoo Azam, Grall Amélie, Malcolm Paul, Zwiggelaar Reyer
Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.
Division of Molecular Pathology, Institute of Cancer Research (ICR), London SM2 5NG, UK.
J Imaging. 2020 Aug 24;6(9):83. doi: 10.3390/jimaging6090083.
The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.
在前列腺的三维磁共振成像（MRI）中手动勾勒感兴趣区域（RoI）既耗时又主观。准确识别前列腺组织有助于在诊断成像、放射治疗和疾病进展监测的临床实践中，定义一个精确的RoI，以便在CAD系统中使用。研究了条件生成对抗网络（cGAN）、循环生成对抗网络（cycleGAN）和U-Net模型及其在三维多参数MRI扫描中检测和分割前列腺组织的性能。这些模型在来自40例经活检证实为前列腺癌患者的MRI数据上进行训练和评估。由于可用训练数据量有限，提出了三种增强方案来人工增加训练样本。这些模型在为本研究标注的临床数据集和一个公共数据集（PROMISE12）上进行了测试。由于包含配对图像监督，cGAN模型的预测性能优于U-Net和cycleGAN。根据我们的定量结果，cGAN在私有数据集和PROMISE12公共数据集上的Dice分数分别为0.78和0.75。
