Rationale and Objectives
Breast tumor segmentation based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging
is significant step for computable radiomics analysis of breast cancer. Manual tumor
annotation is time-consuming process and involves medical acquaintance, biased, inclined
to error, and inter-user discrepancy. A number of modern trainings have revealed the
capability of deep learning representations in image segmentation.
Materials and Methods
Here, we describe a 3D Connected-UNets for tumor segmentation from 3D Magnetic Resonance
Imagings based on encoder-decoder architecture. Due to a restricted training dataset
size, a variational auto-encoder outlet is supplementary to renovate the input image
itself in order to identify the shared decoder and execute additional controls on
its layers. Based on initial segmentation of Connected-UNets, fully connected 3D provisional
unsystematic domain is used to enhance segmentation outcomes by discovering 2D neighbor
areas and 3D volume statistics. Moreover, 3D connected modules evaluation is used
to endure around large modules and decrease segmentation noise.
Results
The proposed method has been assessed on two widely offered datasets, explicitly INbreast
and the curated breast imaging subset of digital database for screening mammography
The proposed model has also been estimated using a private dataset.
Conclusion
The experimental results show that the proposed model outperforms the state-of-the-art
methods for breast tumor segmentation.
Key Words
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Article info
Publication history
Published online: February 16, 2023
Accepted:
December 21,
2022
Received in revised form:
December 18,
2022
Received:
September 19,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
