Publicacións

@inproceedings{botana_hybrid_2026,

title = {Hybrid Morphology-Based Tumor Detection from Breast MRI Segmentation Masks},

author = {Sergio Botana and Paula Puerta González and Pablo García Marcos and Sara Fernández Arias and Rebeca Oliveira Suárez and Guillermo Lorenzo and Héctor Gómez and Covadonga Camino and Víctor M. González and Angel Rio-Alvarez},

editor = {Aurelio López Fernández and Alejandro Rodríguez-González and Raquel Leirós-Rodríguez and Christian Mata Miquel and Víctor Manuel González Suárez},

doi = {10.1007/978-3-032-10661-2_26},

isbn = {978-3-032-10661-2},

year  = {2026},

date = {2026-01-01},

booktitle = {Artificial Intelligence in Biomedicine},

pages = {340–351},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Magnetic resonance imaging (MRI) is widely used to diagnose breast cancer. However, the high sensitivity of dynamic contrast-enhanced MRI (DCE-MRI) comes at the cost of increased complexity in image interpretation and segmentation, which requires advanced techniques to help radiologists delineate tumor boundaries accurately and efficiently. This work proposes an automated methodology that reduces the reliance on manual annotations by integrating unsupervised clustering with supervised classification to identify the tumor cluster within masks derived from clustering techniques using only morphological characteristics, thus facilitating rapid, accurate, and reproducible tumor detection in breast magnetic resonance imaging.},

keywords = {Breast cancer, Morphology-based classification, Tumor classification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gonzalez_unsupervised_2025,

title = {Unsupervised Fuzzy C-Means-Based Approach for Automatic Breast Tumor Segmentation in DCE-MRI},

author = {Paula Puerta González and Pablo García Marcos and Sara Fernández Arias and Rebeca Oliveira Suárez and Guillermo Lorenzo and Héctor Gómez and Covadonga Camino and José R. Villar and Ángel Rio-Alvarez and Víctor M. González},

url = {https://ieeexplore.ieee.org/document/11058745},

doi = {10.1109/CBMS65348.2025.00019},

issn = {2372-9198},

year  = {2025},

date = {2025-06-01},

urldate = {2026-03-23},

booktitle = {2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)},

pages = {49–52},

abstract = {Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, being the most frequently diagnosed cancer among women and the second leading cause of cancer-related mortality. Precise tumor segmentation is essential for breast cancer assessment, as it enables accurate estimation of tumor size, monitoring of disease progression, and evaluation of treatment effectiveness. Despite the importance of this task, the development of reliable automatic methods is hindered by the scarcity of fully annotated datasets, which makes manual labeling both time-consuming and subject to inter-observer variability. In this study, we propose an unsupervised 3D tumor segmentation method based on Fuzzy C-Means (FCM) clustering, specifically designed for volumetric Dynamic Contrast-Enhanced MRI (DCE-MRI) of the breast. Unlike supervised deep learning approaches, our method does not require manual annotations for training, making it especially valuable in scenarios with limited labeled data. The proposed pipeline combines preprocessing, region-of-interest extraction, and FCM-based clustering to generate accurate segmentation masks with minimal human intervention. We evaluated our approach using clinical data from the ACRIN-6698 dataset, comparing the automatic segmentations against expert manual annotations. The method achieved high performance across multiple metrics, including accuracy, precision, recall, specificity, Dice-Sørensen coefficient (DSC), and Jaccard index (IoU). These results demonstrate the feasibility of unsupervised clustering techniques for volumetric breast tumor segmentation, offering a promising alternative to supervised methods in clinical contexts where annotated data is limited.},

note = {ISSN: 2372-9198},

keywords = {Accuracy, Annotations, Breast cancer, Breast tumors, DCE, Fully-automatic segmentation, Fuzzy C-Means, Magnetic resonance imaging, Manuals, MRI, Pipelines, Reliability, Three-dimensional displays, Training, tumor segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}