Authors:
Idriss Cabrel Tsewalo Tondji
1
;
2
;
Francesca Lizzi
2
;
Camilla Scapicchio
2
and
Alessandra Retico
2
Affiliations:
1
Department of Computer Science, University of Pisa, Pisa, Italy
;
2
National Institute for Nuclear Physics, Pisa, Italy
Keyword(s):
Computed Tomography, Deep Learning, Pancreas Segmentation.
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 evaluate
d 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.
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