Scholarlink

<p>Breast cancer is one of the leading causes of cancer-related deaths among women worldwide, with approximately 2.3 million diagnoses and 685,000 deaths in 2020. Early-stage breast cancer is often managed through breast-conserving surgery (BCS) combined with radiation therapy, which aims to preserve the breast’s appearance while reducing recurrence risks. This study aimed to enhance intraoperative tumor segmentation using digital breast tomosynthesis (DBT) during BCS. A deep learning model, specifically an improved U-Net architecture incorporating a convolutional block attention module (CBAM), was utilized to delineate tumor margins with high precision. The system was evaluated on 51 patient cases by comparing automated segmentation with manually delineated contours and pathological assessments. Results showed that the proposed method achieved promising accuracy, with Intersection over Union (IoU) and Dice coefficients of 0.866 and 0.928, respectively, demonstrating its potential to improve intraoperative margin assessment and surgical outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated Tumor Segmentation in Breast-Conserving Surgery Using Deep Learning on Breast Tomosynthesis

  • 期刊论文

  • 发表日期：

    2025年3月3日

查看全文
  • Wen-Pei Wu,
  • Yu-Wen Chen,
  • Hwa-Koon Wu,
  • Dar-Ren Chen,
  • Yu-Len Huang

摘要

Breast cancer is one of the leading causes of cancer-related deaths among women worldwide, with approximately 2.3 million diagnoses and 685,000 deaths in 2020. Early-stage breast cancer is often managed through breast-conserving surgery (BCS) combined with radiation therapy, which aims to preserve the breast’s appearance while reducing recurrence risks. This study aimed to enhance intraoperative tumor segmentation using digital breast tomosynthesis (DBT) during BCS. A deep learning model, specifically an improved U-Net architecture incorporating a convolutional block attention module (CBAM), was utilized to delineate tumor margins with high precision. The system was evaluated on 51 patient cases by comparing automated segmentation with manually delineated contours and pathological assessments. Results showed that the proposed method achieved promising accuracy, with Intersection over Union (IoU) and Dice coefficients of 0.866 and 0.928, respectively, demonstrating its potential to improve intraoperative margin assessment and surgical outcomes.