2.5D Deep Learning Model with Attention Mechanism for Pancreas Segmentation on CT Scans

Idriss Cabrel Tsewalo Tondji, Idriss Cabrel Tsewalo Tondji, Francesca Lizzi, Camilla Scapicchio, Alessandra Retico

2025

Abstract

The accurate segmentation of the irregularly shaped pancreas on Computed Tomography (CT) scans, consisting of 3D images, is a crucial but difficult part of the diagnostic evaluation of pancreatic cancer. Most current deep learning (DL) methods tend to focus on the pancreas or the tumor separately. However, these methods often struggle because the pancreas region is affected by the surrounding complex and low-contrast tissues. This study aims to develop a DL system for pancreas segmentation to improve early detection of tumors. Recognizing the powerful performance with computational demands of 3D models, 2D models appear to be an alternative in terms of computation with a lightweight structure but they disregard the inter-slice correlation which affects the performance. To address this, we are investigating the effect of the data preparation by using a multi-channel input image on the pancreas segmentation model, which is referred to as 2.5D model. Our method is developed and evaluated on a widely used public dataset, the Medical Segmentation Decathlon (MSD) pancreas segmentation dataset. The 2.5D model demonstrates superior performance, reaching a Dice Similarity Coefficient of 75.1%, surpassing the 2D segmentation model, while remaining computationally efficient.

Paper Citation

in Harvard Style

Tsewalo Tondji I., Lizzi F., Scapicchio C. and Retico A. (2025). 2.5D Deep Learning Model with Attention Mechanism for Pancreas Segmentation on CT Scans. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3, SciTePress, pages 669-675. DOI: 10.5220/0013314500003911

in Bibtex Style

@conference{bioinformatics25,
author={Idriss Cabrel Tsewalo Tondji and Francesca Lizzi and Camilla Scapicchio and Alessandra Retico},
title={2.5D Deep Learning Model with Attention Mechanism for Pancreas Segmentation on CT Scans},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={669-675},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013314500003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - 2.5D Deep Learning Model with Attention Mechanism for Pancreas Segmentation on CT Scans
SN - 978-989-758-731-3
AU - Tsewalo Tondji I.
AU - Lizzi F.
AU - Scapicchio C.
AU - Retico A.
PY - 2025
SP - 669
EP - 675
DO - 10.5220/0013314500003911
PB - SciTePress