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MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour

Camila Zambrano¹, Noel Pérez-Pérez¹, Miguel Coimbra²

¹Colegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador.

Life (Basel, Switzerland)

|May 4, 2026

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This summary is machine-generated.

This study presents the MassSeg-Framework, an automated mammography tool for breast mass detection and segmentation. It uses YOLOv11 and Chan-Vese ACM for accurate analysis with efficient performance, aiding early cancer detection.

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Artificial Intelligence in Healthcare

Background:

Mammography is crucial for breast cancer screening.
Accurate breast mass detection and segmentation are vital for diagnosis.
Existing methods often require significant computational resources or manual input.

Purpose of the Study:

To develop a fully automatic, two-stage pipeline for breast mass analysis in mammography.
To achieve accurate mass localization and segmentation with a lightweight computational footprint.
To provide a scalable solution for mammography analysis, especially in resource-constrained settings.

Main Methods:

Integration of YOLOv11-based object detection with Chan-Vese Active Contour Model (ACM) for segmentation.

Keywords:

active contour model breast cancer breast mass detection breast mass segmentation deep learning

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Training and evaluation on publicly available datasets (dINbreast and dCBIS).

Utilizing YOLOv11-nano architecture with a confidence threshold of 0.4 for detection.

Main Results:

YOLOv11-nano achieved high mean Average Precision (mAP50) of 0.862 (dINbreast) and 0.709 (dCBIS).
Segmentation stage yielded mean DICE scores of 0.721 (dINbreast) and 0.700 (dCBIS).
The framework demonstrated superior efficiency compared to state-of-the-art methods.

Conclusions:

The MassSeg-Framework offers an effective and computationally efficient solution for automated breast mass analysis.
The moderate detection threshold preserves clinically relevant findings, crucial for screening.
The pipeline's balance of performance and efficiency makes it suitable for high-throughput and resource-limited environments.