Background: Accurate liver segmentation from abdominal computed tomography (CT) images is an important task for surgical planning, volumetric analysis, and tumor assessment. Although recent deep learning-based three-dimensional segmentation approaches provide high segmentation performance, these models generally require high computational resources and long training times. Methods: In this study, a hybrid liver segmentation framework combining the 3D U-Net architecture with the extreme learning machine (ELM) method was proposed. In the proposed approach, deep volumetric feature maps extracted from the bottleneck layer of the trained 3D U-Net were used as input to an ELM-based classifier for final segmentation refinement. All experiments were performed on the Task03Liverᵣs dataset, which is a rescaled version of the Medical Segmentation Decathlon liver dataset. To provide a more reliable evaluation, fivefold cross-validation experiments were conducted using the same preprocessing pipeline, training protocol, and hyperparameter settings for all comparison models. In addition to overlap-based metrics, boundary-based and clinically relevant metrics including HD95, ASD, surface Dice, and volumetric error were also evaluated. Results: Experimental results demonstrated that the proposed 3D U-Net–ELM framework achieved competitive and stable segmentation performance compared with nnU-Net, standard 3D U-Net, SwinUNet, and SwinUNet–ELM models. The proposed model achieved a mean Dice score of 0. 9399 ± 0. 0210 and an IoU score of 0. 8874 ± 0. 0358 under fivefold cross-validation. Furthermore, the proposed approach produced lower HD95 and ASD values together with higher surface Dice scores, indicating improved boundary consistency and volumetric segmentation quality. In addition, the hybrid ELM-based structure provided advantages in computational efficiency and training cost. Conclusions: The obtained findings indicate that the proposed 3D U-Net–ELM framework provides a balanced and computationally efficient alternative for volumetric liver segmentation. Nevertheless, the absence of independent multicenter external validation remains an important limitation of the study. Future studies will focus on evaluating the proposed framework using larger and more diverse multicenter datasets to further investigate its clinical applicability and generalizability.
Ogut et al. (Sun,) studied this question.