F2PASeg通过特征融合提升垂体手术中解剖结构分割精度。
F2PASeg: Feature Fusion for Pituitary Anatomy Segmentation in Endoscopic Surgery
- 融合高分辨率图像特征与深层语义信息，增强对遮挡等干扰的鲁棒性。
- 在7,845张标注图像上实现实时精准分割，关键结构识别准确率显著提升。
- 适用于垂体微创手术导航，尤其适合需高精度解剖定位的临床场景。
垂体肿瘤常导致邻近重要结构变形或包裹。解剖结构分割可为外科医生提供术中高风险区域预警，提升手术安全性。然而，垂体手术像素级标注视频数据集极为稀缺。为此，我们构建了新的垂体解剖分割数据集（PAS），包含从120段视频中提取的7,845帧时序一致图像。为缓解类别不平衡问题，采用数据增强技术模拟手术器械在训练数据中的出现。垂体解剖分割的一大挑战是因遮挡、摄像机运动和术中出血导致特征表示不一致。为此，提出F2PASeg模型，引入特征融合模块，利用高分辨率图像特征与深层语义嵌入，提升对术中变化的鲁棒性。实验表明，F2PASeg能实时稳定分割关键解剖结构，为术中垂体手术规划提供可靠解决方案。代码开源：https://github.com/paulili08/F2PASeg。
原文摘要 · Abstract (English)
Pituitary tumors often cause deformation or encapsulation of adjacent vital structures. Anatomical structure segmentation can provide surgeons with early warnings of regions that pose surgical risks, thereby enhancing the safety of pituitary surgery. However, pixel-level annotated video stream datasets for pituitary surgeries are extremely rare. To address this challenge, we introduce a new dataset for Pituitary Anatomy Segmentation (PAS). PAS comprises 7,845 time-coherent images extracted from 120 videos. To mitigate class imbalance, we apply data augmentation techniques that simulate the presence of surgical instruments in the training data. One major challenge in pituitary anatomy segmentation is the inconsistency in feature representation due to occlusions, camera motion, and surgical bleeding. By incorporating a Feature Fusion module, F2PASeg is proposed to refine anatomical structure segmentation by leveraging both high-resolution image features and deep semantic embeddings, enhancing robustness against intraoperative variations. Experimental results demonstrate that F2PASeg consistently segments critical anatomical structures in real time, providing a reliable solution for intraoperative pituitary surgery planning. Code: https://github.com/paulili08/F2PASeg.
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