School of Artificial Intelligence, Xidian University, Xi'An, 710071, China.
Department of Interventional Radiology, Tangdu Hospital, Airforce Medical University, Xi'an, 710038, China.
Comput Biol Med. 2024 May;174:108420. doi: 10.1016/j.compbiomed.2024.108420. Epub 2024 Apr 6.
BACKGROUND AND OBJECTIVE: Liver tumor segmentation (LiTS) accuracy on contrast-enhanced computed tomography (CECT) images is higher than that on non-contrast computed tomography (NCCT) images. However, CECT requires contrast medium and repeated scans to obtain multiphase enhanced CT images, which is time-consuming and cost-increasing. Therefore, despite the lower accuracy of LiTS on NCCT images, which still plays an irreplaceable role in some clinical settings, such as guided brachytherapy, ablation, or evaluation of patients with renal function damage. In this study, we intend to generate enhanced high-contrast pseudo-color CT (PCCT) images to improve the accuracy of LiTS and RECIST diameter measurement on NCCT images. METHODS: To generate high-contrast CT liver tumor region images, an intensity-based tumor conspicuity enhancement (ITCE) model was first developed. In the ITCE model, a pseudo color conversion function from an intensity distribution of the tumor was established, and it was applied in NCCT to generate enhanced PCCT images. Additionally, we design a tumor conspicuity enhancement-based liver tumor segmentation (TCELiTS) model, which was applied to improve the segmentation of liver tumors on NCCT images. The TCELiTS model consists of three components: an image enhancement module based on the ITCE model, a segmentation module based on a deep convolutional neural network, and an attention loss module based on restricted activation. Segmentation performance was analyzed using the Dice similarity coefficient (DSC), sensitivity, specificity, and RECIST diameter error. RESULTS: To develop the deep learning model, 100 patients with histopathologically confirmed liver tumors (hepatocellular carcinoma, 64 patients; hepatic hemangioma, 36 patients) were randomly divided into a training set (75 patients) and an independent test set (25 patients). Compared with existing tumor automatic segmentation networks trained on CECT images (U-Net, nnU-Net, DeepLab-V3, Modified U-Net), the DSCs achieved on the enhanced PCCT images are both improved compared with those on NCCT images. We observe improvements of 0.696-0.713, 0.715 to 0.776, 0.748 to 0.788, and 0.733 to 0.799 in U-Net, nnU-Net, DeepLab-V3, and Modified U-Net, respectively, in terms of DSC values. In addition, an observer study including 5 doctors was conducted to compare the segmentation performance of enhanced PCCT images with that of NCCT images and showed that enhanced PCCT images are more advantageous for doctors to segment tumor regions. The results showed an accuracy improvement of approximately 3%-6%, but the time required to segment a single CT image was reduced by approximately 50 %. CONCLUSIONS: Experimental results show that the ITCE model can generate high-contrast enhanced PCCT images, especially in liver regions, and the TCELiTS model can improve LiTS accuracy in NCCT images.
背景与目的：与非增强计算机断层扫描（NCCT）图像相比，肝脏肿瘤分割（LiTS）在增强计算机断层扫描（CECT）图像上的准确性更高。然而，CECT 需要造影剂并重复扫描以获得多期增强 CT 图像，这既耗时又增加成本。因此，尽管 LiTS 在 NCCT 图像上的准确性较低，但它在某些临床环境中仍然具有不可替代的作用，例如引导近距离放射治疗、消融或评估肾功能损害患者。在这项研究中，我们旨在生成增强的高对比度伪彩色 CT（PCCT）图像，以提高 LiTS 在 NCCT 图像上的准确性和 RECIST 直径测量的准确性。
方法：为了生成高对比度 CT 肝肿瘤区域图像，首先开发了基于强度的肿瘤显著性增强（ITCE）模型。在 ITCE 模型中，建立了肿瘤强度分布的伪彩色转换函数，并将其应用于 NCCT 以生成增强的 PCCT 图像。此外，我们设计了一种基于肿瘤显著性增强的肝脏肿瘤分割（TCELiTS）模型，用于提高 NCCT 图像上的肝脏肿瘤分割准确性。TCELiTS 模型由三个部分组成：基于 ITCE 模型的图像增强模块、基于深度卷积神经网络的分割模块和基于受限激活的注意损失模块。使用 Dice 相似系数（DSC）、敏感性、特异性和 RECIST 直径误差来分析分割性能。
结果：为了开发深度学习模型，将 100 名经组织病理学证实的肝脏肿瘤患者（肝细胞癌 64 例，肝血管瘤 36 例）随机分为训练集（75 例）和独立测试集（25 例）。与在 CECT 图像上训练的现有肿瘤自动分割网络（U-Net、nnU-Net、DeepLab-V3、Modified U-Net）相比，在增强的 PCCT 图像上实现的 DSC 均优于在 NCCT 图像上实现的 DSC。我们观察到 U-Net、nnU-Net、DeepLab-V3 和 Modified U-Net 的 DSC 值分别提高了 0.696-0.713、0.715-0.776、0.748-0.788 和 0.733-0.799。此外，还进行了一项包括 5 位医生的观察者研究，以比较增强 PCCT 图像和 NCCT 图像的分割性能，结果表明增强 PCCT 图像更有利于医生分割肿瘤区域。结果显示，准确性提高了约 3%-6%，但分割单个 CT 图像所需的时间减少了约 50%。
结论：实验结果表明，ITCE 模型可以生成高对比度增强的 PCCT 图像，特别是在肝脏区域，而 TCELiTS 模型可以提高 NCCT 图像上的 LiTS 准确性。
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