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: this https URL

通过引入点邻域学习、增强训练监督及扩充数据多样性，我们提出了一种弱半监督方法，名为 Point-Neighborhood Learning (PNL) 框架，用于医学图像中的点注释，显著提高了性能并不改变分割网络结构。