A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation (2502.00314v1)

Abstract: The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.

• The paper introduces novel U-Net modifications that enhance segmentation accuracy with an average Dice Similarity Coefficient of 0.9309 on retroperitoneal tumor CT data.
• The paper compares various architectures, including CNNs, ViTs, Mamba, and xLSTM modifications, highlighting the superior performance of the U-Net-xLSTM model.
• The paper provides a new CT dataset with expertly annotated segmentation maps that underpin robust validation and guide future integrations of multi-modal imaging techniques.

An Evaluation of U-Net Modifications for Retroperitoneal Tumor Segmentation

The paper "A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation" investigates the effectiveness of different U-Net-based modifications for the segmentation of retroperitoneal tumors using CT imaging data. Recognizing the challenges associated with automated tumor segmentation, due to the complex anatomical structures and large volumetric data, the paper introduces and evaluates novel architectures to improve segmentation accuracy and computational efficiency.

The authors compare various architectures, including Convolutional Neural Networks (CNNs), Vision Transformer (ViT)-based models, and innovative approaches like the Mamba State Space Model and Extended Long Short-Term Memory (xLSTM) networks, applied within the U-Net framework. The introduction of ViLU-Net, an architecture that integrates ViT blocks into the encoder-decoder structure of U-Net, demonstrates advancement in biomedical image segmentation tasks. Notably, the paper introduces a new dataset focused on retroperitoneal tumors, with meticulously annotated segmentation masks generated by expert radiologists, which provides a strong foundation for robust validation of the proposed methods.

Numerical Results and Contributions

The performance evaluation encompassed the newly introduced in-house CT dataset alongside publicly available datasets for comparison. The xLSTM-based U-Net architecture displayed superior performance, achieving an average Dice Similarity Coefficient (DSC) score of 0.9309 on the retroperitoneal tumor dataset, outperforming other models such as the CNN-based nnU-Net and the transformer-based SwinUNETR. Additionally, the xLSTM-enabled architecture yielded a normalized surface distance (NSD) of 0.9292 and exhibited robustness in maintaining lower Hausdorff distances, underpinning its efficacy in accurate boundary delineation.

This paper's key contributions include:

1. The creation and provision of a potent CT dataset specifically targeting retroperitoneal tumors with annotated segmentation maps.
2. A comprehensive comparison of contemporary deep learning methodologies for segmentation, incorporating CNNs, ViTs, Mamba, and the new xLSTM modifications.
3. Validation of a U-Net-xLSTM network demonstrating competitive or superior performance coupled with reduced complexity.

Implications and Future Avenues

The research contributes significantly to the field of medical image analysis by introducing an effective model that balances segmentation precision and computational efficiency. These findings bolster the application of xLSTM within medical imaging contexts, suggesting a potentially transformative approach to handling large-scale volumetric data while reducing computational burdens.

Future research avenues could focus on enhancing the integration of multiple imaging modalities, such as MRI and PET, within hybrid segmentation frameworks to improve diagnostic accuracy. Additionally, further exploration into multi-modal fusion techniques that account for the heterogeneity of medical imaging data could drive the development of models with even more refined segmentation capabilities. Moreover, initiatives to refine the interpretability and trustworthiness of AI-driven analysis in clinical settings will be crucial for broader adoption of these advanced methodologies in personalized medicine.

Overall, this paper underscores the potential of novel U-Net modifications to address the challenges of medical image segmentation in contexts where traditional methods are often hamstrung by data complexity and processing limitations. The open-source availability of codes and datasets signals an invitation to the research community to further evolve AI-driven medical imaging applications, fortifying the advancements presented in this paper.