  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.
