Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was primarily applied to single-modality non-contrast-enhanced liver MRI and additionally to the publicly available computed tomography (CT) data of the liver tumor segmentation (LiTS) challenge to verify the applicability on other modalities. For a broader evaluation, multiple state-of-the-art networks were implemented and applied, ensuring a direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various influencing factors on the segmentation accuracy of the presented method. With Dice scores of averaged 98+-2% for liver and 81+-28% lesion segmentation on the MRI dataset and 97+-2% and 79+-25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging. The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.

翻译：基于深度学习的肝脏及肝病灶分割因每年肝癌发病率的增加而在临床实践中日益重要。近年来，尽管医学图像分割领域已成功开发出多种整体性能优异的网络变体，但几乎所有方法在磁共振成像（MRI）中精确分割肝病灶方面仍面临挑战。这促使研究者提出融合卷积神经网络与Transformer架构元素的方案以突破现有局限。本文提出一种名为SWTR-Unet的混合网络，其包含预训练ResNet、Transformer模块以及常规Unet风格解码路径。该网络主要应用于单模态非增强肝脏MRI，同时应用于公开的肝脏肿瘤分割（LiTS）挑战赛的计算机断层扫描（CT）数据以验证其跨模态适用性。为进行广泛评估，我们实现并应用了多种最新网络以确保直接可比性。此外，通过相关性分析和消融实验，探究了影响所提方法分割精度的多种因素。在MRI数据集上，SWTR-Unet的肝脏及病灶分割平均Dice分数分别为98±2%和81±28%；在CT数据集上分别为97±2%和79±25%，证明其作为肝脏及肝病灶精确分割方法的有效性——在MRI上达到最新技术水平，在CT上具有竞争性精度。通过与肝病灶分割的观察者间变异比较，发现所实现的分割精度可媲美人工专家分割结果。结论表明，所提方法可为临床实践节省宝贵时间与资源。