A Study on the Performance of U-Net Modifications in Retroperitoneal
  Tumor Segmentation
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.
67a1d1ca167bea74d520eb59
Moein Heidari
admin_assigned
2025-02-04T21:12:31.574Z
61ba19bf6122a4fd29049371
https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
Moein Heidari
user
moein99
67a1d1ca167bea74d520eb5a
Ehsan Khodapanah Aghdam
67a1d1ca167bea74d520eb5b
Alexander Manzella
67a1d1ca167bea74d520eb5c
Daniel Hsu
67a1d1ca167bea74d520eb5d
Rebecca Scalabrino
67a1d1ca167bea74d520eb5e
Wenjin Chen
admin_assigned
2025-02-04T21:13:15.403Z
6793160813ed9a38f3c214ef
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/qVy6X8N_ckK5Z-y2ger_2.png
wenjin chen
user
cwjbks
67a1d1ca167bea74d520eb5f
David J. Foran
67a1d1ca167bea74d520eb60
Ilker Hacihaliloglu
A Study on the Performance of U-Net Modifications in Retroperitoneal
  Tumor Segmentation
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 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.
U-Net ViT Mamba State Space Model Extended Long-Short Term Memory xLSTM ViLU-Net CNN