A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation

TL;DR

This paper introduces a point-neighborhood learning framework for nasal endoscope image segmentation that leverages surrounding context to improve weakly semi-supervised segmentation performance with minimal annotation effort.

Contribution

The paper proposes a novel weakly semi-supervised learning method called PNL that incorporates surrounding neighborhood information and a new supervision loss, enhancing segmentation accuracy.

Findings

PNL outperforms existing methods on NPC-LES dataset.

PNL achieves significant performance improvements with minimal additional parameters.

Validation on colonoscopic datasets confirms PNL's generalizability.

Abstract

Lesion segmentation on nasal endoscopic images is challenging due to its complex lesion features. Fully-supervised deep learning methods achieve promising performance with pixel-level annotations but impose a significant annotation burden on experts. Although weakly supervised or semi-supervised methods can reduce the labelling burden, their performance is still limited. Some weakly semi-supervised methods employ a novel annotation strategy that labels weak single-point annotations for the entire training set while providing pixel-level annotations for a small subset of the data. However, the relevant weakly semi-supervised methods only mine the limited information of the point itself, while ignoring its label property and surrounding reliable information. This paper proposes a simple yet efficient weakly semi-supervised method called the Point-Neighborhood Learning (PNL) framework. PNL incorporates the surrounding area of the point, referred to as the point-neighborhood, into the learning process. In PNL, we propose a point-neighborhood supervision loss and a pseudo-label scoring mechanism to explicitly guide the model's training. Meanwhile, we proposed a more reliable data augmentation scheme. The proposed method significantly improves performance without increasing the parameters of the segmentation neural network. Extensive experiments on the NPC-LES dataset demonstrate that PNL outperforms existing methods by a significant margin. Additional validation on colonoscopic polyp segmentation datasets confirms the generalizability of the proposed PNL.