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

基本事实 分割 人工智能 深度学习 计算机科学 注释 最小边界框 相关系数 乳腺癌 磁共振成像 相关性 人口 Sørensen–骰子系数 模式识别(心理学) 图像分割 机器学习 医学 数学 癌症 放射科 图像(数学) 几何学 内科学 环境卫生
作者
Ga Eun Park,Sung Hun Kim,Yoonho Nam,Junghwa Kang,Minjeong Park,Bong Joo Kang
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:59 (6): 2252-2262 被引量:4
标识
DOI:10.1002/jmri.28960
摘要

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
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