A Study on the Performance of U-Net Modifications in Retroperitoneal
  Tumor Segmentation
Authors: name: Moein Heidari, name: Ehsan Khodapanah Aghdam, name: Alexander Manzella, name: Daniel Hsu, name: Rebecca Scalabrino, name: Wenjin Chen, name: David J. Foran, name: Ilker Hacihaliloglu

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.