A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation
This work addresses segmentation challenges for rare retroperitoneal tumors, which is incremental as it builds on existing U-Net variants with new architectures.
This study tackled the problem of automatic segmentation of retroperitoneal tumors in CT scans, which is challenging due to irregular shapes and high computational demands, by evaluating U-Net modifications including CNN, ViT, Mamba, and xLSTM, and found that xLSTM was efficient in this framework.
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.