Robust Rat Liver Lobe Segmentation in Low-SNR T2-weighted Datasets Using a 2.5D Approach

Motivation: Our project was motivated by the lack of an efficient way of segmenting ligated and non-ligated liver lobes in portal vein ligation (PVL) experiments. Goal(s): Our goal was to demonstrate that a 2.5D segmentation approach can achieve precise and robust lobe segmentation in experimental PVL volumetry to reduce manual annotation work. Approach: We stacked adjacent slices as input and trained a U-Net to segment the rat liver lobes using 15 rat T2-weighted datasets. Results: An average Dice score of 0.707 was reached by 5-fold cross validation on 15 datasets, showing the robustness in low-SNR MR images with high intensity variation. Impact: We demonstrate the 2.5D approach is robust in segmenting liver lobes with varied intensity in low-SNR MR images. The framework can greatly reduce manual annotation work even with limited datasets.