获取途径

作者

Liang Qiu Wenhao Chi Xiaohan Xing Praveenbalaji Rajendran Mingjie Li Yuming Jiang Oscar Pastor-Serrano Sen Yang Yuanfeng Ji Qiang Wen

作者单位

¹Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA ²Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA ³Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA ⁶Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan 250021, China ⁵Ant Group, CA 94085, USA ⁴Department of Radiation Oncology, Wake Forest University, Winston-Salem, NC 27109, USA

刊名

Biomedical Signal Processing and Control

年份

2026

卷号

Vol.120 Part B

页码

110100

ISSN

1746-8094

摘要

Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic segments, known as Couinaud segmentation, is challenging due to indistinct sub-region boundaries and the need for extensive annotated datasets. This study introduces LiverFormer, a novel Couinaud segmentation model that effectively integrates global c...更多

Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic segments, known as Couinaud segmentation, is challenging due to indistinct sub-region boundaries and the need for extensive annotated datasets. This study introduces LiverFormer, a novel Couinaud segmentation model that effectively integrates global context with low-level local features based on a 3D hybrid CNN-Transformer architecture. Additionally, a registration-based data augmentation strategy is equipped to enhance the segmentation performance with limited labeled data. Evaluated on CT images from a development cohort with additional external validation, LiverFormer demonstrated high accuracy and strong concordance with expert annotations across multiple metrics. These results establish LiverFormer as a reliable and standardized Couinaud segmentation framework, providing a methodological basis for future studies related to surgical and radiotherapy planning.收起

文献类型

期刊

浏览量

3