The lesion segmentation on endoscopic images is challenging due to its complex and ambiguous features. Fully-supervised deep learning segmentation methods can receive good performance based on entirely pixel-level labeled dataset but greatly increase experts' labeling burden. Semi-supervised and weakly supervised methods can ease labeling burden, but heavily strengthen the learning difficulty. To alleviate this difficulty, weakly semi-supervised segmentation adopts a new annotation protocol of adding a large number of point annotation samples into a few pixel-level annotation samples. However, existing methods only mine points' limited information while ignoring reliable prior surrounding the point annotations. In this paper, we propose a weakly semi-supervised method called Point-Neighborhood Learning (PNL) framework. To mine the prior of the pixels surrounding the annotated point, we transform a single-point annotation into a circular area named a point-neighborhood. We propose point-neighborhood supervision loss and pseudo-label scoring mechanism to enhance training supervision. Point-neighborhoods are also used to augment the data diversity. Our method greatly improves performance without changing the structure of segmentation network. Comprehensive experiments show the superiority of our method over the other existing methods, demonstrating its effectiveness in point-annotated medical images. The project code will be available on: https://github.com/ParryJay/PNL.

翻译：内窥镜图像中的病灶分割因其复杂且模糊的特征而具有挑战性。全监督的深度学习分割方法可以基于完全像素级标注的数据集获得良好性能，但极大地增加了专家的标注负担。半监督和弱监督方法可以减轻标注负担，但显著增加了学习难度。为了缓解这一困难，弱半监督分割采用了一种新的标注协议，即在少量像素级标注样本中加入大量点标注样本。然而，现有方法仅挖掘点的有限信息，而忽略了点标注周围可靠的先验信息。在本文中，我们提出了一种名为点邻域学习（PNL）框架的弱半监督方法。为了挖掘标注点周围像素的先验信息，我们将单点标注转换为一个称为点邻域的圆形区域。我们提出了点邻域监督损失和伪标签评分机制以增强训练监督。点邻域也被用于增强数据多样性。我们的方法在不改变分割网络结构的情况下极大地提高了性能。综合实验表明，我们的方法优于其他现有方法，证明了其在点标注医学图像中的有效性。项目代码将发布于：https://github.com/ParryJay/PNL。