Chen Yizheng, Xing Lei, Yu Lequan, Bagshaw Hilary P, Buyyounouski Mark K, Han Bin
Department of Radiation Oncology, Stanford University, Stanford, 94305, USA.
Med Phys. 2020 Dec;47(12):6421-6429. doi: 10.1002/mp.14517. Epub 2020 Oct 24.
Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to clinical practice.
Multiparametric magnetic resonance imaging images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4. The contours of the lesion and prostate were manually created on axial T2-weighted (T2W), apparent diffusion coefficient (ADC) and high b-value diffusion-weighted imaging (DWI) images to provide the ground truth data. Then a multiple branch UNet (MB-UNet) was proposed for the segmentation of an indistinct target in multi-modality MRI images. An encoder module was designed with three branches for the three MRI modalities separately, to fully extract the high-level features provided by different MRI modalities; an input module was added by using three sub-branches for three consecutive image slices, to consider the contour consistency among different image slices; deep supervision strategy was also integrated into the network to speed up the convergency of the network and improve the performance. The probability maps of the background, normal prostate and lesion were output by the network to generate the segmentation of the lesion, and the performance was evaluated using the dice similarity coefficient (DSC) as the main metric.
A total of 162 lesions were contoured on 652 image slices, with 119 lesions in the peripheral zone, 38 in the transition zone, four in the central zone and one in the anterior fibromuscular stroma. All prostates were also contoured on 1,264 image slices. As for the segmentation of lesions in the testing set, MB-UNet achieved a per case DSC of 0.6333, specificity of 0.9993, sensitivity of 0.7056; and global DSC of 0.7205, specificity of 0.9993, sensitivity of 0.7409. All the three deep learning strategies adopted in this study contributed to the performance promotion of the MB-UNet. Missing the DWI modality would degrade the segmentation performance more markedly compared with the other two modalities.
A deep learning-based approach with proposed MB-UNet was developed to automatically segment suspicious lesions in mpMRI images. This study makes it feasible to adopt boosting intraprostatic lesions in clinical practice to achieve better outcomes.
勾勒前列腺内病变轮廓是在放射治疗中对这些病变进行剂量递增以改善局部癌症控制的前提条件。在本研究中，开发了一种基于深度学习的方法，用于在多参数磁共振成像（mpMRI）图像中自动分割前列腺内病变，以促进临床实践。
从我们机构收集了136例患者的多参数磁共振成像图像，所有这些病例均包含前列腺影像报告和数据系统（PI-RADS）评分≥4的可疑病变。在轴向T2加权（T2W）、表观扩散系数（ADC）和高b值扩散加权成像（DWI）图像上手动创建病变和前列腺的轮廓，以提供真实数据。然后提出了一种多分支UNet（MB-UNet）用于多模态MRI图像中不清晰目标的分割。设计了一个编码器模块，分别有三个分支用于三种MRI模态，以充分提取不同MRI模态提供的高级特征；通过使用三个子分支对连续的三个图像切片添加一个输入模块，以考虑不同图像切片之间的轮廓一致性；还将深度监督策略集成到网络中，以加速网络收敛并提高性能。网络输出背景、正常前列腺和病变的概率图以生成病变分割，并使用骰子相似系数（DSC）作为主要指标评估性能。
在652个图像切片上共勾勒出162个病变，其中外周区119个病变，移行区38个，中央区4个，前纤维肌基质区1个。所有前列腺也在1264个图像切片上进行了轮廓勾勒。对于测试集中病变的分割，MB-UNet实现了每例DSC为0.6333，特异性为0.9993，敏感性为0.7056；全局DSC为0.7205，特异性为0.9993，敏感性为0.7409。本研究采用的所有三种深度学习策略都有助于提高MB-UNet的性能。与其他两种模态相比，遗漏DWI模态会更明显地降低分割性能。
开发了一种基于深度学习的方法及所提出的MB-UNet，用于在mpMRI图像中自动分割可疑病变。本研究使得在临床实践中采用增强前列腺内病变以获得更好结果成为可能。
