3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation

Ga Eun Park; Sung Hun Kim; Yoonho Nam; Junghwa Kang; Minjeong Park; Bong Joo Kang

doi:10.1002/jmri.28960

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation

J Magn Reson Imaging. 2023 Aug 19. doi: 10.1002/jmri.28960. Online ahead of print.

Authors

Ga Eun Park¹, Sung Hun Kim¹, Yoonho Nam², Junghwa Kang², Minjeong Park², Bong Joo Kang¹

Affiliations

¹ Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
² Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.

PMID: 37596823
DOI: 10.1002/jmri.28960

Abstract

Background: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.

Purpose: To develop a deep learning model for 3D breast cancer segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using weak annotation with reliable performance.

Study type: Retrospective.

Population: Seven hundred and thirty-six women with breast cancer from a single institution, divided into the development (N = 544) and test dataset (N = 192).

Field strength/sequence: 3.0-T, 3D fat-saturated gradient-echo axial T1-weighted flash 3D volumetric interpolated brain examination (VIBE) sequences.

Assessment: Two radiologists performed a weak annotation of the ground truth using bounding boxes. Based on this, the ground truth annotation was completed through autonomic and manual correction. The deep learning model using 3D U-Net transformer (UNETR) was trained with this annotated dataset. The segmentation results of the test set were analyzed by quantitative and qualitative methods, and the regions were divided into whole breast and region of interest (ROI) within the bounding box.

Statistical tests: As a quantitative method, we used the Dice similarity coefficient to evaluate the segmentation result. The volume correlation with the ground truth was evaluated with the Spearman correlation coefficient. Qualitatively, three readers independently evaluated the visual score in four scales. A P-value <0.05 was considered statistically significant.

Results: The deep learning model we developed achieved a median Dice similarity score of 0.75 and 0.89 for the whole breast and ROI, respectively. The volume correlation coefficient with respect to the ground truth volume was 0.82 and 0.86 for the whole breast and ROI, respectively. The mean visual score, as evaluated by three readers, was 3.4.

Data conclusion: The proposed deep learning model with weak annotation may show good performance for 3D segmentations of breast cancer using DCE-MRI.

Level of evidence: 3 TECHNICAL EFFICACY: Stage 2.

Keywords: breast cancer; deep learning; segmentation.