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 (MpaU-Net)\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{paU}}-{\text{Net}}})$$\end{document} and 3d full resolution of nnU-Net (MnnU-Net)\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{nnU}}-{\text{Net}}})$$\end{document} to determine the best architecture (BA)\documentclass[12pt]{minimal}
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\begin{document}$${\text{BA}})$$\end{document}. BA was used with vessels (MVess)\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{Vess}}})$$\end{document} and spleen (Mseg+spleen)\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{seg}}+{\text{spleen}}})$$\end{document} to assess the impact on segment contouring. Models were trained, validated, and tested on 160 (CRTTrain\documentclass[12pt]{minimal}
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\begin{document}$${{\text{C}}}_{{\text{RTTrain}}}$$\end{document}), 40 (CRTVal\documentclass[12pt]{minimal}
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\begin{document}$${{\text{C}}}_{{\text{RTVal}}}$$\end{document}), 33 (CLS\documentclass[12pt]{minimal}
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\begin{document}$${{\text{C}}}_{{\text{LS}}}$$\end{document}), 25 (CCH) and 20 (CPVE) CECT of LC patients. MnnU-Net\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{nnU}}-{\text{Net}}}$$\end{document} outperformed MpaU-Net\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{paU}}-{\text{Net}}}$$\end{document} across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03–0.05 (p < 0.05). Mseg+spleen\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{seg}}+{\text{spleen}}}$$\end{document}, and MnnU-Net\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{nnU}}-{\text{Net}}}$$\end{document} were not statistically different (p > 0.05), however, both were slightly better than MVess\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{Vess}}}$$\end{document} by DSC up to 0.02. The final model, Mseg+spleen\documentclass[12pt]{minimal}
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\begin{document}$${{\text{M}}}_{{\text{seg}}+{\text{spleen}}}$$\end{document}, 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% of cases showed a Likert score ≥\documentclass[12pt]{minimal}
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\begin{document}$$\ge$$\end{document} 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.
