用AI精准分割肝脏区域，提升肝癌治疗精度
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy
- 融合3D CNN与Transformer，结合全局与局部特征
- 在123例患者数据上达到专家级分割准确率
- 适合肝癌手术与放疗规划，降低治疗损伤
肝癌精准治疗需精确划分肝脏亚区以保护健康组织并靶向肿瘤，这对降低复发率、提高生存率至关重要。然而，由于亚区边界模糊且需大量标注数据，Couinaud分割面临挑战。本研究提出LiverFormer，一种基于3D混合CNN-Transformer架构的新模型，有效融合全局上下文与低层局部特征。同时引入基于配准的数据增强策略，在有限标注数据下提升分割性能。在123名患者的CT图像上评估，LiverFormer在多种指标下表现出高精度与专家标注强一致性，显著优化外科与放疗的治疗方案，有望减少并发症，最小化对周围组织的损伤，改善复杂肝癌治疗预后。
原文摘要 · Abstract (English)
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 123 patients, LiverFormer demonstrated high accuracy and strong concordance with expert annotations across various metrics, allowing for enhanced treatment planning for surgery and radiation therapy. It has great potential to reduces complications and minimizes potential damages to surrounding tissue, leading to improved outcomes for patients undergoing complex liver cancer treatments.
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