Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway; Department of Informatics, University of Oslo, 0315 Oslo, Norway.
Artif Intell Med. 2022 Aug;130:102331. doi: 10.1016/j.artmed.2022.102331. Epub 2022 Jun 9.
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.
深度学习方法，特别是卷积神经网络和全卷积网络，现在在医学图像分析领域得到了广泛应用。本次综述的范围侧重于使用深度学习分析局灶性肝病变，特别关注肝细胞癌和转移性癌症；以及实质或脉管系统等结构。在这里，我们讨论了几种用于分析来自不同成像方式（如计算机断层扫描、磁共振成像和超声）的肝脏的解剖结构和病变的神经网络架构。讨论了用于肝脏、肝血管和肝病变的分割、目标检测和分类等图像分析任务。通过定性搜索，筛选出 91 篇论文进行综述，包括期刊出版物和会议论文集。本文综述的论文根据所使用的方法分为八类。通过比较评估指标，混合模型在肝脏和病变分割任务中表现更好，集成分类器在血管分割任务中表现更好，联合方法在病变分类和检测任务中表现更好。性能是基于分割的 Dice 得分、分类和检测任务的准确性来衡量的，这是最常用的指标。
