Abstract
Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions. In this work, we propose SegMatch, a semi supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi supervised classification pipeline combining consistency regularization and pseudo labelling, and adapts it for the purpose of segmentation. In our proposed SegMatch, the unlabelled images are weakly augmented and fed into the segmentation model to generate a pseudo-label to enforce the unsupervised loss against the output of the model for the adversarial augmented image on the pixels with a high confidence score. Our adaptation for segmentation tasks includes carefully considering the equivariance and invariance properties of the augmentation functions we rely on. To increase the relevance of our augmentations, we depart from using only handcrafted augmentations and introduce a trainable adversarial augmentation strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate that adding unlabelled data for training purposes allows us to surpass the performance of fully supervised approaches which are limited by the availability of training data in these challenges. SegMatch also outperforms a range of state-of-the-art semi-supervised learning semantic segmentation models in different labelled to unlabelled data ratios.
Abstract (translated)
surgical instrument segmentation 被认为是提供高级手术辅助和提高计算机辅助干预的关键工具。在这项工作中,我们提出了 segMatch 半监督学习方法,以降低对Laparoscopic 和机器人手术图像的昂贵标记的依赖性。SegMatch 建立在 fixMatch 普遍使用的半监督分类管道,结合一致性 Regularization 和伪标签,并适应于分割任务。在我们提出的 segMatch 中,未标记的图像进行弱增强,并输入到分割模型,生成伪标签,以强制模型输出对具有高信心值像素的无监督损失与模型的输出。我们对分割任务的调整包括仔细考虑我们依赖的增强函数的等温性和不变性性质。为了增加我们的增强函数的相关性,我们离开了仅使用手工增强和引入训练可增强的对抗增强策略。我们的方法在 MICCAI 设备分割挑战数据集 Robust-MIS 2019 和EndoVis 2017 中进行评估。我们的结果表明,为训练目的添加未标记数据可以使我们超过这些挑战中完全监督方法的性能限制。此外, segMatch 在不同类型的标记数据比例下表现出比当前最先进的半监督学习语义分割模型更好的性能。
URL
https://arxiv.org/abs/2308.05232
