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:深度学习基于肝脏和其中肝脏病变的分割由于每年肝癌日益增加而在临床实践中稳步增加。尽管在医学图像分割领域成功地开发出了各种具有总体有希望的结果的网络变体,但它们几乎都在准确分割磁共振成像(MRI)中的肝脏病变的挑战中遇到困难。这促使了将卷积和Transformer 网络结构的元素组合起来以克服现有限制的想法。本研究提出了一种混合网络,称为SWTR-Unet,由预训练的ResNet,Transformer块以及常用的Unet--decoder路径组成。该网络主要应用于非对比增强的肝脏MRI,此外还应用于公开可用的肝脏肿瘤分割(LiTS)挑战的计算机断层扫描(CT)数据,以验证方法在其他模态下的适用性。为了进行更广泛的评估,实施和应用了多种最先进的网络,确保直接比较。此外,还进行了相关性分析和消融研究,以调查对所提出方法的分割精度的各种影响因素。在MRI数据集上,SWTR-Unet的Dice分数平均为98 ± 2%和81 ± 28%进行肝脏和病变分割,而在CT数据集上分别为97 ± 2%和79 ± 25%,证明了它是一种精确的肝脏和肝脏病变分割方法,可获得MRI的最新结果和CT成像竞争精度。所达到的分割精度与手动进行的专家分割相当,这表明肝脏病变分割的多个观察者之间的变异性。总之,本研究所提出的方法可以节省临床实践中宝贵的时间和资源。