title: A Study on the Performance of U-Net Modifications in Retroperitoneal
    Tumor Segmentation
summary: 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.
paper_authors._id: 67a1d1ca167bea74d520eb59
paper_authors.name: Moein Heidari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T21:12:31.574Z
paper_authors._id: 61ba19bf6122a4fd29049371
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
paper_authors.fullname: Moein Heidari
paper_authors.type: user
paper_authors.user: moein99
paper_authors._id: 67a1d1ca167bea74d520eb5a
paper_authors.name: Ehsan Khodapanah Aghdam
paper_authors._id: 67a1d1ca167bea74d520eb5b
paper_authors.name: Alexander Manzella
paper_authors._id: 67a1d1ca167bea74d520eb5c
paper_authors.name: Daniel Hsu
paper_authors._id: 67a1d1ca167bea74d520eb5d
paper_authors.name: Rebecca Scalabrino
paper_authors._id: 67a1d1ca167bea74d520eb5e
paper_authors.name: Wenjin Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T21:13:15.403Z
paper_authors._id: 6793160813ed9a38f3c214ef
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/qVy6X8N_ckK5Z-y2ger_2.png
paper_authors.fullname: wenjin chen
paper_authors.type: user
paper_authors.user: cwjbks
paper_authors._id: 67a1d1ca167bea74d520eb5f
paper_authors.name: David J. Foran
paper_authors._id: 67a1d1ca167bea74d520eb60
paper_authors.name: Ilker Hacihaliloglu
paper_title: A Study on the Performance of U-Net Modifications in Retroperitoneal
    Tumor Segmentation
paper_summary: 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.
paper_ai_summary: The study evaluates enhanced U-Net models incorporating CNN, ViT, Mamba, and xLSTM for automatic segmentation of retroperitoneal tumors, with xLSTM demonstrating efficiency in U-Net.
paper_ai_keywords: U-Net ViT Mamba State Space Model Extended Long-Short Term Memory xLSTM ViLU-Net CNN