## 5 Discussion and Conclusions
The model can selectively enable or disable certain encoder stages by analyzing the gradient change rates across the input volume. This adaptive mechanism will optimize resource allocation, ensuring that the computational effort is focused only on the required stages, thus striking a better balance between efficiency and performance. These approaches will offer promising paths to further enhance the practicality and scalability of G-UNETR++, particularly for deployment in resource-constrained environments. During our model development stage, we found that false positive and false negative errors appeared. False positive errors usually occur between the liver and its adjacent organs such as the stomach, spleen, and heart due to the overlap between their intensity values. To address this issue, we applied the connected components algorithm and the median filter to reduce false positive errors. To deal with false negative errors that usually occur within some small regions near the tumor boundaries, we proposed our hybrid loss function, especially by including HD loss to enhance boundary delineation by focusing on spatial accuracy at the edges of segmented regions. The hybrid loss improved liver segmentation accuracy. The proposed G-UNETR++ showed significant generalizability over datasets from different sources for liver segmentation. Future research will be conducted to apply the proposed model for the segmentation of other organs, such as brain tumors and multiple organs from the abdomen, for not only CT scans, but also MRI scans. In conclusion, the proposed G-UNETR++ is effective for liver segmentation using CT scans with high accuracy, robustness, and generalizability with a moderate complexity level. Future studies will focus on further reducing the complexity level and broadening the applications of our model. **Author Contributions:** Conceptualization, S.L., K.H. and X.Y. ; methodology, S.L., K.H. and X.Y. ; software, S.L., K.H. and H.S. ; validation, S.L., K.H., H.S. and X.Y. ; formal analysis, S.L. and K.H. ; investigation, S.L., K.H., H.S. and X.Y. ; resources, X.Y., J.D.Y., H.C.Y. and H.Y. ; data curation, S.L., H.P. and J.D.Y. ; writing--original draft preparation, S.L., K.H., H.S., H.P., S.K., J.K. and X.Y. ; writing--review and editing, S.L., K.H., H.P., S.K., J.K., X.Y. and H.Y. ; visualization, S.L., K.H. and X.Y. ; supervision, X.Y. and H.Y. ; project administration, S.L., X.Y. and H.Y. ; funding acquisition, X.Y., J.D.Y., H.C.Y. and H.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by No. 202300720001 of the Handong Global University Research Grants, Fund of Biomedical Research Institute, Jeonbuk National University Hospital, and a grant from the Mid-Career Researcher Program through the National Research Foundation (NRF) of Korea (2022R1A2C1013198). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The LITS dataset is available at [[https://competitions.codalab.org/competitions/17094](https://competitions.codalab.org/competitions/17094)]([https://competitions.codalab.org/competitions/17094](https://competitions.codalab.org/competitions/17094)) (accessed on 30 October 2024). The 3D-IRCADb dataset is available at [[https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01](https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01)]([https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01](https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01)) (accessed on 30 October 2024). The Sliver07 dataset is available at [[https://sliver07.grand-challenge.org](https://sliver07.grand-challenge.org)]([https://sliver07.grand-challenge.org](https://sliver07.grand-challenge.org)) (accessed on 30 October 2024). **Conflicts of Interest:** The authors declare no conflicts of interest.