准确的前列腺解剖对于低位直肠癌患者的经肛门手术至关重要。解剖不当会导致尿道损伤等不良事件，严重影响患者术后康复。然而，边界不清晰、前列腺形状不规则以及烟雾等阻塞因素给外科医生带来了巨大的挑战。我们的创新贡献在于引入了一种新颖的视频语义分割框架IG-Net，该框架融合了现有手术器械的特征，以实现真实的手术效果。 -时间和精确的前列腺分割。具体来说，我们设计了一个仪器引导模块，该模块根据仪器特征计算外科医生的注意力区域，执行局部分割，并将其与全局分割相结合以提高性能。此外，我们提出了一个关键帧选择模块，该模块根据仪器特征计算连续帧之间的时间相关性。该模块自适应地选择非关键帧进行特征融合分割，降低噪声并优化速度。为了评估 IG-Net 的性能，我们构建了迄今为止已知的最广泛的数据集，包括 106 个视频剪辑和 6153 张图像。实验结果表明，该方法取得了良好的性能，IoU 为 72.70%，Dice 为 82.02%，FPS 为 35。对于基于手术视频的前列腺分割任务，我们提出的 IG-Net 在多个指标上超越了以前的所有方法。 IG-Net 平衡了分割准确性和速度，表现出针对不利因素的强大鲁棒性。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Accurate prostate dissection is crucial in transanal surgery for patients with low rectal cancer. Improper dissection can lead to adverse events such as urethral injury, severely affecting the patient's postoperative recovery. However, unclear boundaries, irregular shape of the prostate, and obstructive factors such as smoke present significant challenges for surgeons.Our innovative contribution lies in the introduction of a novel video semantic segmentation framework, IG-Net, which incorporates prior surgical instrument features for real-time and precise prostate segmentation. Specifically, we designed an instrument-guided module that calculates the surgeon's region of attention based on instrument features, performs local segmentation, and integrates it with global segmentation to enhance performance. Additionally, we proposed a keyframe selection module that calculates the temporal correlations between consecutive frames based on instrument features. This module adaptively selects non-keyframe for feature fusion segmentation, reducing noise and optimizing speed.To evaluate the performance of IG-Net, we constructed the most extensive dataset known to date, comprising 106 video clips and 6153 images. The experimental results reveal that this method achieves favorable performance, with 72.70% IoU, 82.02% Dice, and 35 FPS.For the task of prostate segmentation based on surgical videos, our proposed IG-Net surpasses all previous methods across multiple metrics. IG-Net balances segmentation accuracy and speed, demonstrating strong robustness against adverse factors.Copyright © 2024 Elsevier B.V. All rights reserved.