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.

翻译：手术器械分割被公认为是提供高级手术辅助和改善计算机辅助介入的关键技术。本研究提出了一种名为SegMatch的半监督学习方法，旨在减少腹腔镜和机器人手术图像昂贵标注的需求。SegMatch基于FixMatch（一种结合了一致性正则化和伪标签的广泛使用的半监督分类框架），并将其改造用于分割任务。在我们提出的SegMatch中，未标注图像经过弱增强后输入分割模型，生成伪标签，针对高置信度得分的像素，利用对抗增强图像上的模型输出施加无监督损失。针对分割任务的适配，我们仔细考虑了所依赖增强函数的等变性和不变性属性。为提升增强的相关性，我们摒弃仅使用手工设计的增强方法，引入了一种可训练的对抗性增强策略。该算法在MICCAI器械分割挑战数据集Robust-MIS 2019和EndoVis 2017上进行了评估。结果表明，利用未标注数据进行训练使我们能够超越完全监督方法的性能，而这些方法受限于这些挑战中训练数据的可获得性。此外，在不同标注与未标注数据比例下，SegMatch亦优于一系列最先进的半监督学习语义分割模型。