Liver Segmentation in Computed Tomography Images Using Transformers, Inception Module and U-Net

Hepatic surgery requires the segmentation of the liver from computed tomography (CT) images. Fully automated approaches are required since manual segmentation requires much time and labor. This paper proposes a deep learning model with the basic architecture U-Net to segment the liver, including a transformer and an inception modules. The Dice coefficient on the test phase was 84.8% and the Jaccard coefficient was 63.6% on 332 CT images. This work shows the usefulness of the transformers in the liver segmentation.