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Updated: Jul 1, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

[Segmentation of Mass in Mammogram Using Gaze Search Patterns].

Eiichiro Okumura¹, Hideki Kato², Tsuyoshi Honmoto³

¹Department of Radiological Technology, Faculty of Health Sciences, Tsukuba International University.

Nihon Hoshasen Gijutsu Gakkai Zasshi

|March 13, 2024

Summary

This summary is machine-generated.

This study explored using eye-tracking focus images to improve mammogram mass segmentation. Combining UNIT-generated focus images with original mammograms achieved segmentation performance comparable to using original mammograms alone.

Keywords:

eye-tracking focus images mammogram segmentation of mass unsupervised image-to-image translation network (UNIT)

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Last Updated: Jul 1, 2025

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Area of Science:

Medical Imaging
Computer Vision
Radiology

Context:

Mammography interpretation is challenging for detecting small or obscured lesions in dense breast tissue.
Computer-aided detection (CAD) systems are increasingly used to aid radiologists.
Accurate segmentation of masses is crucial for effective CAD systems.

Purpose:

To accurately segment mammographic masses using focus images generated by an eye-tracking device.
To evaluate the performance of different deep learning models (auto-encoder, Pix2Pix, UNIT) in generating focus images.
To assess the combined efficacy of focus images and original mammograms for mass segmentation.

Summary:

Focus images were generated using auto-encoder, Pix2Pix, and UNIT models trained on radiologist eye-tracking data.
The UNIT model achieved a higher Dice coefficient (0.64±0.14) compared to auto-encoder and Pix2Pix.
Combining UNIT-generated focus images with original mammograms yielded a Dice coefficient of 0.66±0.15, comparable to using original mammograms alone.

Impact:

This research explores novel methods for enhancing mammogram analysis through eye-tracking data integration.
The findings suggest potential for improving the accuracy of computer-aided detection systems.
Further research with larger datasets is needed to optimize segmentation performance.