DeepPyram：在白内障手术视频中启用金字塔视图和可变形金字塔接收以进行语义分割
DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos
摘要：
白内障手术中的语义分割具有广泛的应用，有助于提高手术效果和降低临床风险。然而，在分割不同的相关实例时存在的各种问题使得指定一个独特的网络非常具有挑战性。本文提出了一种称为DeepPyram的语义分割网络，该网络可以在白内障手术视频中分割具有不同问题的相关对象时实现卓越的性能。这种优越性主要源于三个模块：（i）金字塔视图融合，其提供以输入卷积特征图中的每个像素位置为中心的周围区域的不同角度的全局视图;（ii）可变形金字塔接收，其实现可以适应感兴趣对象中的几何变换的宽的可变形感受野;以及（iii）自适应监督多尺度语义特征映射的金字塔损失。这些模块可以有效地提高语义分割性能，特别是在对象的透明性、可变形性、可伸缩性和钝边的情况下。所提出的方法进行评估，使用四个数据集的白内障手术的对象具有不同的上下文功能，并与13个国家的最先进的分割网络进行比较。实验结果证实，DeepPyram优于竞争对手的方法，而无需施加额外的可训练参数。我们全面的消融研究进一步证明了所提出的模块的有效性。
英文摘要：
Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant instances make the designation of a unique network quite challenging. This paper proposes a semantic segmentation network termed as DeepPyram that can achieve superior performance in segmenting relevant objects in cataract surgery videos with varying issues. This superiority mainly originates from three modules: (i) Pyramid View Fusion, which provides a varying-angle global view of the surrounding region centering at each pixel position in the input convolutional feature map; (ii) Deformable Pyramid Reception, which enables a wide deformable receptive field that can adapt to geometric transformations in the object of interest; and (iii) Pyramid Loss that adaptively supervises multi-scale semantic feature maps. These modules can effectively boost semantic segmentation performance, especially in the case of transparency, deformability, scalability, and blunt edges in objects. The proposed approach is evaluated using four datasets of cataract surgery for objects with different contextual features and compared with thirteen state-of-the-art segmentation networks. The experimental results confirm that DeepPyram outperforms the rival approaches without imposing additional trainable parameters. Our comprehensive ablation study further proves the effectiveness of the proposed modules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
