abstract
Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering. Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity. Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.
subject category
Computer Science
authors
Pereira, TMC; Pelicano, AC; Godinho, DM; Gonçalves, MCT; Castela, T; Orvalho, ML; Sencadas, V; Sebastiao, R; Conceiçao, RC
our authors
Teresa Margarida de Campos Pereira
PhD Student
Vitor Sencadas
Associate ProfessorProjects
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
CICECO - Aveiro Institute of Materials (UIDP/50011/2020)
Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)
acknowledgements
This work was funded by national funds through FCT Fundacao para a Ciencia e a Tecnologia, I.P., under the PhD grant UI/BD/153605/2022 (T.M.C.P.), the Scientific Employment Stimulus CEECIND/03986/2018 (R.S.) and CEECINST/00013/2021 (R.S.), and within the R&D units IEETA/UA UIDB/00127/2020, IBEB UIDB/00645/2020, 2022.08973.PTDC, and CICECO-Aveiro Institute of Materials UIDB/50011/2020, UIDP/50011/2020, and LA/P/0006/2020 (PIDDAC).