Selection of Backbone for Feature Extraction with U-Net in Pancreas Segmentation

Alexandre Araújo, Joao Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva, Geraldo Braz Junior

2024

Abstract

The survival rate for pancreatic cancer is among the worst, with a mortality rate of 98%. Diagnosis in the early stage of the disease is the main factor that defines the prognosis. Imaging scans, such as Computerized Tomography scans, are the primary tools for early diagnosis. Computer Assisted Diagnosis tools that use these scans usually include in their pipeline the segmentation of the pancreas as one of the initial steps for diagnosis. This paper presents a comparative study of the use of different backbones in combination with the U-Net. This study aims to demonstrate that using pre-trained backbones is a valuable tool for pancreas segmentation and to provide a comparative benchmark for this task. The best result obtained was 85.96% of Dice in the MSD dataset for the pancreas segmentation using backbone efficientnetb7.

Paper Citation

in Harvard Style

Araújo A., Dallyson Sousa de Almeida J., Cardoso de Paiva A. and Braz Junior G. (2024). Selection of Backbone for Feature Extraction with U-Net in Pancreas Segmentation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 822-829. DOI: 10.5220/0012573900003660

in Bibtex Style

@conference{visapp24,
author={Alexandre Araújo and Joao Dallyson Sousa de Almeida and Anselmo Cardoso de Paiva and Geraldo Braz Junior},
title={Selection of Backbone for Feature Extraction with U-Net in Pancreas Segmentation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={822-829},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012573900003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Selection of Backbone for Feature Extraction with U-Net in Pancreas Segmentation
SN - 978-989-758-679-8
AU - Araújo A.
AU - Dallyson Sousa de Almeida J.
AU - Cardoso de Paiva A.
AU - Braz Junior G.
PY - 2024
SP - 822
EP - 829
DO - 10.5220/0012573900003660
PB - SciTePress