Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ( M_paU-Net) and 3d full resolution of nnU-Net ( M_nnU-Net) to determine the best architecture ( BA) . BA was used with vessels ( M_Vess) and spleen ( M_seg+spleen) to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ( C_RTTrain ), 40 ( C_RTVal ), 33 ( C_LS ), 25 (C CH ) and 20 (C PVE ) CECT of LC patients. M_nnU-Net outperformed M_paU-Net across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03–0.05 (p < 0.05). M_seg+spleen , and M_nnU-Net were not statistically different (p > 0.05), however, both were slightly better than M_Vess by DSC up to 0.02. The final model, M_seg+spleen , showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5–8, and spleen, respectively on entire test sets. Qualitatively, more than 85