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Updated: Apr 18, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction
Daniel Sleiman1, Navchetan Awasthi1,2
1University of Amsterdam, Informatics Institute, Faculty of Science, Mathematics and Computer Science, Amsterdam, The Netherlands.
Purpose:
Magnetic resonance imaging (MRI), particularly dynamic contrast-enhanced MRI (DCE-MRI), plays a vital role in breast cancer assessment by highlighting tumor regions. Accurate 3D segmentation of tumors can significantly aid in diagnosis, disease monitoring, and treatment planning. Current state-of-the-art models such as nnU-Net are computationally expensive, with high parameter counts and memory requirements. In this work, we propose a parameter-efficient convolutional neural network-based architecture tailored for breast tumor segmentation in DCE-MRI.
Approach:
The model integrates lightweight residual blocks into the SegResNet backbone and is trained on the first 3 DCE-MRI phases. We test the addition of the FRLoss from the HCMA-UNet model in the FRLoss ablation. Its encoder-decoder design also enables exploration of the treatment response prediction to neoadjuvant chemotherapy measured by the pathological complete response, using the output of the last encoder block. The last encoder block output is average-pooled and used as input to an XGBoost model with two estimators (max depth 5, learning rate 1).
Results:
Evaluated on the public MAMA-MIA dataset, our proposed model achieves comparable performance to nnU-Net and SegResNet, with a 0.99% higher Dice score than nnU-Net, while reducing parameter count by 91.5%, FLOPs by 85.05%, and memory usage by 31.94% compared with nnU-Net. Therefore, the proposed model is significantly more efficient than nnU-Net and also offers superior accuracy to the Mamba-based baseline, even though the Mamba baseline remains computationally lighter demonstrated by its faster inference speed and lower giga floating point operations per second (38 versus 87). The XGBoost model for treatment response prediction does not demonstrate competitive performance with a balanced accuracy of 57.2% and receiver operating characteristic-area under curve of 0.542.
Conclusion:
Our results demonstrate that parameter-efficient models can achieve competitive performance in DCE-MRI tumor segmentation.
