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Published in: International Journal of Computer Assisted Radiology and Surgery 3/2020

01-03-2020 | Computed Tomography | Original Article

Deep multi-scale feature fusion for pancreas segmentation from CT images

Authors: Zhanlan Chen, Xiuying Wang, Ke Yan, Jiangbin Zheng

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 3/2020

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Abstract

Purpose

Pancreas segmentation from computed tomography (CT) images is an important step in surgical procedures such as cancer detection and radiation treatment. While manual segmentation is time-consuming and operator-dependent, current computer-assisted segmentation methods are facing challenges posed by varying shapes and sizes. To address these challenges, this paper presents a multi-scale feature fusion (MsFF) model for accurate pancreas segmentation from CT images.

Methods

The proposed MsFF is built upon the well-recognized encoder–decoder framework. Firstly, in the encoder stage, the squeeze-and-excitation module is incorporated to enhance the learning of features by exploiting channel-wise independence. Secondly, a hierarchical fusion module is introduced to better utilize both low-level and high-level features to retain boundary information and make final predictions.

Results

The proposed MsFF is evaluated on the NIH pancreas dataset and outperforms the current state-of-the-art methods, by achieving a mean of 87.26% and 22.67% under the Dice Sorensen Coefficient and Volumetric Overlap Error, respectively.

Conclusion

The experimental results confirm that the incorporation of squeeze-and-excitation and hierarchical fusion modules contributes to a net gain in the performance of our proposed MsFF.
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Metadata
Title
Deep multi-scale feature fusion for pancreas segmentation from CT images
Authors
Zhanlan Chen
Xiuying Wang
Ke Yan
Jiangbin Zheng
Publication date
01-03-2020
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 3/2020
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-020-02117-y

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