Purpose. Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. Method. In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. Results. The mean DSC of DCNN-F, DFN-F, and DFN-R was (median, 0.72), (median, 0.77), and (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was (median, 0.83), which was lower than the performance of DFN-R. Conclusions. Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.

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深度学习结合放射科医生干预实现双相磁共振图像肝细胞癌精准分割

目的。肝细胞癌 (HCC) 的分割至关重要；然而，手动分割是主观且耗时的。临床实践中需要准确、自动的 HCC 病灶轮廓绘制。为了满足这一需求，我们的研究引入了一种结合深度卷积神经网络 (DCNN) 和磁共振成像 (MRI) 放射科医生干预的 HCC 分割方法。我们试图设计一种采用深度学习方法的分割方法，该方法可以使用手动位置信息为经验丰富的放射科医生自动进行分割。此外，我们验证了该方法协助放射科医生准确快速进行病变分割的可行性。方法。在我们的研究中，我们开发了一种使用 DCNN 结合放射科医生干预双相钆-乙氧基苯甲基-二亚乙基三胺五乙酸 (Gd-EOB-DTPA-) 增强 MRI 来分割 HCC 的半自动方法。我们开发了在全尺寸图像上训练的 DCNN 和深度融合网络（DFN），即 DCNN-F 和 DFN-F。此外，DFN被应用于由具有10年腹部MRI经验的放射科医生粗略勾勒出的包含肿瘤病灶的图像块，这种方法被命名为DFN-R。另一位拥有五年经验（中等经验）的放射科医生进行了肿瘤病灶轮廓绘制，以与我们提出的方法进行比较。地面真实图像由经验丰富的放射科医生绘制轮廓，并由经验丰富的独立放射科医生进行审查。结果。DCNN-F、DFN-F 和 DFN-R 的平均 DSC 为（中位数，0.72），（中位数，0.77），以及（中位数，0.88）。具有中等经验的放射科医生进行分割的平均 DSC 为（中位数，0.83），低于 DFN-R 的性能。结论。使用双相 MRI 的深度学习显示出 HCC 病灶分割的巨大潜力。与具有中等经验的放射科医生的手动轮廓相比，放射科医生辅助的半自动方法（DFN-R）取得了更好的性能，尽管差异不具有统计学意义。