Meeting Banner
Abstract #4311

Deep Learning-enabled Fully Automated 3D DCE-MRI Segmentation for Breast Cancer Lesion

Ruixin Zhang1, Kaiting Wang1, Sicong Huang2, Jingyu Xie3, Shiwei Wang1, and Maosheng Xu1
1The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), hangzhou, China, 2Philips Healthcare, Beijing, China, 3Philips Healthcare, Shanghai, China

Synopsis

Keywords: Segmentation, Breast

Motivation: Automating and optimising the segmentation process for breast tumours aims to reduce the workload of radiologists and improve the consistency and reliability of segmentation.

Goal(s): To develop a 3D DCE-MRI-based deep network architecture that would achieve fully automated segmentation of breast cancer lesions.

Approach: A dataset consists of 622 (298+128+196) anonymized DCE–MRI scans was collected. We proposed the attention and transformer based method for 3D breast tumor segmentation, incorporating Residual and UNet modules to enhance feature relevance and capture multi-scale context.

Results: Our models have achieved significant results in automated breast cancer lesion identification and segmentation.

Impact: By constructing and training an efficient deep learning model to achieve high-precision segmentation of breast cancer lesions, it provides a powerful auxiliary tool for clinical diagnosis, treatment planning and prognosis analysis.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords