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Related Experiment Video

Updated: Jun 4, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Parameter map guided explainable segmentation framework for breast cancer using amide proton transfer weighted

Qiuhui Yang^1,2, Meng Wang³, Weiqiang Dou⁴

¹Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Medical Physics

|December 19, 2024

Summary

This summary is machine-generated.

This study introduces a novel multi-task network for segmenting breast lesions in Amide Proton Transfer (APT) imaging. The model leverages varying contrasts in APTw images, improving accuracy and aiding diagnosis.

Keywords:

MRI amide proton transfer weighted imaging breast cancer explainable framework

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Published on: August 14, 2012

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Amide proton transfer weighted (APTw) imaging is crucial for breast cancer diagnosis, treatment evaluation, and prognosis.
Automatic segmentation of breast lesions in APTw images is challenging but necessary for quantification.

Purpose of the Study:

To develop a segmentation model for APTw imaging using original images.
To utilize varying contrasts between lesions and surrounding tissues at different frequency offsets for improved segmentation.

Main Methods:

A multi-task network was proposed, integrating lesion segmentation, pathological classification, and APTw parameter map fitting.
The segmentation model incorporates multiple images at different frequencies to leverage varying tissue contrasts.

Main Results:

The proposed method achieved higher accuracy (ACC) compared to advanced models like U-Net, SAM, Med-SAM, and TransBTS.
Model interpretability was enhanced, showing how varying gray contrasts contribute to segmentation.
Pathological classification and parametric map fitting tasks were shown to improve segmentation accuracy.

Conclusions:

Multi-task learning, specifically pathological classification and parameter fitting, can enhance breast lesion segmentation accuracy in APTw imaging.
The developed model offers a promising approach for automated breast lesion segmentation in clinical settings.