Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion mo...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2023-09, Vol.61 (9), p.2379-2389
Main Authors: Dou, Meng, Chen, Zhebin, Tang, Yuanling, Sheng, Leiming, Zhou, Jitao, Wang, Xin, Yao, Yu
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Cancer
Colorectal cancer
colorectal neoplasms
Computer Applications
Deep learning
Embedding
Human Physiology
image analysis
Image contrast
Image processing
Image segmentation
Imaging
Information processing
Magnetic resonance imaging
Medical imaging
Modules
Original Article
Radiology
Rectum
Redundancy
Tumors
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Summary:Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy. In addition, a deep learning–based segmentation model (AF-UNet) is designed to achieve accurate segmentation of rectal tumors. This model takes multi-parametric MRI images as input and effectively integrates the features from different multi-parametric MRI images by embedding the attention fusion module. Finally, three types of MRI images (T2, ADC, DWI) of 250 patients with rectal cancer were collected, with the tumor regions delineated by two oncologists. The experimental results show that the proposed method is superior to the most advanced image segmentation method with a Dice coefficient of 0.821 ± 0.065 , which is also better than other multi-modal fusion methods. Graphical Abstract Framework of the AF-UNet. This model takes multi-modal MRI images as input, and integrates complementary information using attention mechanism and suppresses redundancy.
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-023-02828-9