MSE-Guided Hybrid U-Net Framework for Automatic Kidney Segmentation and Spatial Localization

Evaluating segmentation results in ultrasound imaging is still difficult due to noise, low contrast, and ambiguity at the boundaries, which makes it very challenging to measure accurately. Mean Squared Error (MSE) is a widely used but highly spatially sensitive evaluation metric for comparing predicted masks and ground truth. This work introduces a Mean Squared Error (MSE) based evaluation framework augmented using Block-Based Region Matching (BBRM) to achieve higher robustness against positional errors. The MSE is calculated under spatial shifts, and the best alignment with the lowest error is identified. To verify the effectiveness of the method, this work uses multiple deep learning segmentation models as baseline methods, along with the U-Net, such as SegNet and DeepLab v3+. Experimental results show that the proposed framework gives better and more reliable error analysis compared to conventional MSE evaluation. The experimental results indicated that the UNet + BBRM framework proposed in this study achieved an MSE of 0.0108, an accuracy of 98.92%, a Dice coefficient of 0.9369, and an IoU of 0.8831 in the segmentation task, respectively, compared with other methods. For the comparison with the local dataset, BBRM reduced the MSE from 0.022 to 0.015 and Dice (IoU) from 0.887 to 0.911 and 0.812 to 0.845. These findings underline the need for distribution-based error analysis and spatial alignment of segmentation methods in medical imaging applications.

@article{Anuar2026,
  title     = {MSE-Guided Hybrid U-Net Framework for Automatic Kidney Segmentation and Spatial Localization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Dannial Asyraf Shahrul Anuar and Nabilah Ibrahim and Audrey Huong},
  doi       = {10.14569/IJACSA.2026.0170623},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170623}
}