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Updated: Jun 24, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Razieh Ganjee¹, Andriy Bandos^1,2, Md Belayat Hossain³

¹Department of Radiology, University of Pittsburgh, 203 Lothrop Street, Pittsburgh, PA, 15237, USA.

Journal of Imaging Informatics in Medicine

|June 22, 2026

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This summary is machine-generated.

A new deep learning model, MammoDenseSegNet, accurately segments dense breast tissue in mammograms. This AI tool significantly improves breast cancer risk assessment, especially for low-density tissue, outperforming existing methods.

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Accurate breast density quantification is crucial for breast cancer risk assessment.
Segmenting dense breast tissue in mammograms is challenging due to variations in appearance and imaging devices.
Existing methods struggle with accuracy, particularly for low-density tissues.

Purpose of the Study:

To develop and evaluate MammoDenseSegNet, a novel deep convolutional neural network for enhanced segmentation of dense breast tissue.
To improve the accuracy and robustness of breast density quantification in mammograms.

Main Methods:

MammoDenseSegNet employs an encoder-decoder architecture with an adaptive dual attention module and a multi-kernel receptive field module.
A multi-scale dice loss with deep supervision was utilized to enhance learning across decoder levels.

Keywords:

BI-RADS density Breast cancer risk assessment Breast density segmentation Deep Learning

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Last Updated: Jun 24, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

The model was evaluated on two public (VinDR-Mammo, EMBED) and one private mammogram dataset (1499 images).

Main Results:

MammoDenseSegNet achieved high performance across diverse conditions (Recall: 0.64-0.90, Dice: 0.63-0.91).
The model significantly outperformed a VGG16-based state-of-the-art algorithm (p < 0.001).
Significant improvements were observed for low-density tissues, where MammoDenseSegNet demonstrated clinical utility (Recall: 0.66, Dice: 0.63) compared to the baseline's failure (Recall: 0.14, Dice: 0.16).

Conclusions:

MammoDenseSegNet offers a robust and accurate solution for dense breast tissue segmentation in mammography.
The proposed deep learning approach enhances breast cancer risk assessment capabilities.
MammoDenseSegNet shows particular promise in improving the analysis of low-density breast tissues.