Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
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Authors
Militello, C
Rundo, L
Dimarco, M
Orlando, A
Conti, V
Woitek, R
D'Angelo, I
Bartolotta, TV
Russo, G
Publication Date
2022-01-01Journal Title
Biomedical Signal Processing and Control
ISSN
1746-8094
Publisher
Elsevier BV
Volume
71
Number
ARTN 103113
Pages
103113-103113
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Militello, C., Rundo, L., Dimarco, M., Orlando, A., Conti, V., Woitek, R., D'Angelo, I., et al. (2022). Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomedical Signal Processing and Control, 71 (ARTN 103113), 103113-103113. https://doi.org/10.1016/j.bspc.2021.103113
Abstract
Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segment masses on dynamic contrast-enhanced (DCE) MRI of the breast. Our method was compared against existing approaches based on classic image processing, namely (i) Otsu's method for thresholding-based segmentation, and (ii) the traditional FCM algorithm. A further comparison was performed against state-of-the-art Convolutional Neural Networks (CNNs) for medical image segmentation, namely SegNet and U-Net, in a 5-fold cross-validation scheme. The results showed the validity of the proposed approach, by significantly outperforming the competing methods in terms of the Dice similarity coefficient (84.47±4.75). Moreover, a Pearson's coefficient of ρ=0.993 showed a high correlation between segmented volume and the gold standard provided by clinicians. Overall, the proposed method was confirmed to outperform the competing literature methods. The proposed computer-assisted approach could be deployed into clinical research environments by providing a reliable tool for volumetric and radiomics analyses.
Keywords
Semi-automated segmentation, Breast cancer, Unsupervised fuzzy clustering, Spatial information, Computer-assisted lesion detection, Magnetic Resonance Imaging
Sponsorship
Mark Foundation for Cancer Research US Ltd (Unknown)
Cancer Research UK (C96/A25177)
National Institute for Health Research (IS-BRC-1215-20014)
Identifiers
External DOI: https://doi.org/10.1016/j.bspc.2021.103113
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335529
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