论文标题
无监督的手术仪器通过锚定和语义扩散进行分割
Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion
论文作者
论文摘要
手术仪器分割是开发上下文感知手术室的关键组成部分。关于这项任务的现有作品在很大程度上依赖于大量标记数据的监督，这些数据涉及艰苦而昂贵的人类努力。相比之下，本文开发了一种更实惠的无监督方法。为了训练我们的模型，我们首先通过融合粗制的手工提示来分别作为仪器和背景组织的锚定标签。然后提出了语义扩散损失，以通过相邻视频帧之间的特征相关性来解决生成的锚点中的歧义。在2017年MICCAI Endovis机器人仪器分割挑战数据集的二进制仪器分割任务的实验中，所提出的方法在不使用单个手动注释的情况下达到0.71 iou和0.81骰子得分，这有望显示出对手术工具进行分裂的无疗法学习的潜力。
Surgical instrument segmentation is a key component in developing context-aware operating rooms. Existing works on this task heavily rely on the supervision of a large amount of labeled data, which involve laborious and expensive human efforts. In contrast, a more affordable unsupervised approach is developed in this paper. To train our model, we first generate anchors as pseudo labels for instruments and background tissues respectively by fusing coarse handcrafted cues. Then a semantic diffusion loss is proposed to resolve the ambiguity in the generated anchors via the feature correlation between adjacent video frames. In the experiments on the binary instrument segmentation task of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset, the proposed method achieves 0.71 IoU and 0.81 Dice score without using a single manual annotation, which is promising to show the potential of unsupervised learning for surgical tool segmentation.
