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:由于每年肝癌发病率的上升,肝脏和肝脏损伤的深层分解在临床实践中稳步增加相关性。虽然过去几年在医学图像分解领域成功开发了各种网络变异,在整体上有望在医学图像分解领域取得良好结果,但几乎所有的网络变异体都面临在磁共振成像(MRI)中准确分解肝脏损伤的挑战。这导致将以变动和变压为基础的结构要素结合起来以克服现有限制的想法。这项工作提出了一个称为SWTR-Unet的混合网络,由预先训练的ResNet、变压器块和通用的内型肝脏分解码路径组成。这个网络主要用于单调非调增生肝脏分解的单调变异异体,另外还用来应对向公众提供的电算断层(CT)数据。通过肝脏肿瘤分解(LitTS)数据来核实其他模式的可适用性。为了更广泛的评估,已经实施并应用了多种SWT-RE-RER-RY 网络,确保直接的可比性。此外,还进行关联性分析,并且分析,并进行一项对98-D-BLE-D-D-D-D-D-D-deal-deal-de-deal-de-de-de-de-de-deal-de-de-de-deal-de-de-de-de-de-de-de-de-de-deal-de-de-de-de-de-de-de-de-deal-deal-deal-deal-deal-de-de-de-de-de-de-de-de-deal-deal-de-de-de-al-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-deal-deal-de-de-de-de-deal-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-de-