深度学习用于微创腹部手术中术中纱布分割的首次研究
First Investigation of Deep Learning for Intraoperative Gauze Segmentation in Minimally Invasive Abdominal Surgery
- Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）
- University of Bonn（波恩大学）
- University Hospital Bonn（波恩大学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用医院内部手术数据集，评估多种深度学习分割架构，探索自动跟踪标注策略，解决纱布分割的数据稀缺问题，为机器人辅助手术提供异物精准分割方案。
AI中文摘要：
手术纱布是外科手术的重要组成部分，主要用于控制出血和吸收体液。术后纱布残留会引发严重并发症，需额外手术取出。尽管具有临床重要性，但由于标注数据集稀缺，利用真实手术数据进行纱布分割的研究仍未得到充分探索。本研究调查深度学习方法在机器人辅助微创腹部手术中纱布分割的应用，使用某大学医院制备的内部手术数据集。训练数据反映真实手术场景，涵盖三类不同纱布在空间、形态和视觉属性上的广泛多样性。我们评估多种广泛使用的分割架构，包括基于卷积神经网络（CNN）、基于Transformer的及混合架构，以在真实临床场景中建立纱布分割的概念验证。此外，我们研究标注不佳的自动跟踪分割掩码的影响，作为解决数据稀缺并提升性能的策略。结果表明，真实训练数据可应对先前研究报告的主要挑战——血液存在与纱布检测间的权衡；加入自动跟踪标注可提升性能，尤其在通用手术场景中。有效分割方法的整合可通过精准描绘异物，助力机器人引导的手术流程及各类下游应用，进而提升患者安全与手术结局。
英文摘要：
Surgical gauze is an essential part of surgical procedures, primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical retention of gauze can lead to serious complications and necessitate additional surgery for its removal. Despite the clinical significance, research on gauze segmentation using real-world surgical data remains underexplored, owing in part to the scarcity of annotated datasets. In this work, we investigate the use of deep learning methods for gauze segmentation in robot-assisted minimally invasive abdominal surgeries, utilizing an in-house surgical dataset prepared at a university hospital. The training data reflects realistic surgical settings and captures extensive diversity in spatial, morphological, and visual attributes across three different gauze categories. We evaluate several widely used segmentation architectures, including CNN-based, transformer-based, and hybrid architectures, to establish a proof-of-concept for gauze segmentation in a realistic clinical setting. In addition, we investigate the influence of sub-optimally annotated, auto-tracked segmentation masks as a strategy to address data scarcity and improve performance. Our results demonstrate the efficacy of real-world training data in countering the main challenge reported by prior works, the trade-off between blood presence and gauze detection. The incorporation of auto-tracked annotations yields performance enhancements, particularly in generic surgical scenarios. The integration of effective segmentation approaches can benefit robot-guided surgical procedures and various downstream applications by providing precise delineation of foreign objects, thereby enhancing patient safety and surgical outcomes.
