Semi-Supervised Medical Image Segmentation Based on Suspicious Pixel Mutual Correction

YANG Zi-yao; LEI Tao; DU Xiao-gang; WANG Meng-xi; SUN Fei-man; GONG Mao-guo

doi:10.12263/DZXB.20240616

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Semi-Supervised Medical Image Segmentation Based on Suspicious Pixel Mutual Correction

PAPERS | 更新时间：2025-08-18

- Semi-Supervised Medical Image Segmentation Based on Suspicious Pixel Mutual Correction
  增强出版
- ACTA ELECTRONICA SINICA Vol. 53, Issue 5, Pages: 1607-1621(2025)
- 作者机构：
  
  1.陕西科技大学电子信息与人工智能学院，陕西西安 710021
  2.陕西省人工智能联合实验室（陕西科技大学），陕西西安 710021
  3.西南交通大学数学学院，四川成都 611756
  4.西安电子科技大学协同智能系统教育部重点实验室，陕西西安 710071
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271296;62201334);Scientific Research Program Funded by Shaanxi Provincial Education Commission(23JP022;23JP014)
- DOI：10.12263/DZXB.20240616
  CLC： TP391;
- Received：02 July 2024，
  
  Revised：2025-04-01，
  
  Published：25 May 2025
- 稿件说明：
移动端阅览
杨子瑶, 雷涛, 杜晓刚, 等. 基于可疑像素相互修正的半监督医学图像分割[J]. 电子学报, 2025, 53(05): 1607-1621.

YANG Zi-yao, LEI Tao, DU Xiao-gang, et al. Semi-Supervised Medical Image Segmentation Based on Suspicious Pixel Mutual Correction[J]. Acta Electronica Sinica, 2025, 53(05): 1607-1621.
杨子瑶, 雷涛, 杜晓刚, 等. 基于可疑像素相互修正的半监督医学图像分割[J]. 电子学报, 2025, 53(05): 1607-1621. DOI：10.12263/DZXB.20240616

YANG Zi-yao, LEI Tao, DU Xiao-gang, et al. Semi-Supervised Medical Image Segmentation Based on Suspicious Pixel Mutual Correction[J]. Acta Electronica Sinica, 2025, 53(05): 1607-1621. DOI：10.12263/DZXB.20240616

摘要

现有的半监督学习方法通常对图像中的所有像素进行同等处理，忽视了图像内不同区域的复杂度差异.这导致模型对于预测难度较高的挑战性区域学习不足，从而降低了其对这些区域的处理能力.此外，由于伪标签是基于当前模型对未标注数据的预测结果生成的，而模型在挑战性区域表现较差，因此不准确的预测增加了伪标签中引入噪声的风险，进而降低了伪标签的可信度.针对上述问题，提出了一种基于可疑像素相互修正的半监督学习框架（Suspicious Pixel Mutual Correction，SPMC）.该框架由两个编码器结构相同，但解码器上采样方式不同的网络构成.首先，设计了一个用于处理标注数据的共困像素筛查（Common Difficulty Pixel Screening，CDPS）模块.通过利用每个网络预测结果中的可疑像素，精准筛查出两个网络都预测困难的共困像素，并使用准确的监督信号对其进行修正，从而提高模型对挑战性区域预测的准确性.其次，设计了一个用于处理未标注数据的软伪标签辅助教学（Soft Pseudo-Label Assisted Teaching，SPLAT）模块.通过利用一个网络生成的软伪标签中的可信像素，选择性地对另一个网络预测结果中的可疑像素进行伪监督.两个网络通过高质量交互来更新参数，从而减少模型的认知偏差并提升伪标签质量.实验结果表明，提出的方法在三种公开医学数据集左心房（Left Atrium，LA）、脑部肿瘤分割（Brain Tumor Segmentation，BraTS）和自动心脏挑战（Automatic Cardiac Diagnosis Challenge，ACDC）上的性能均优于当前主流的半监督学习方法.

Abstract

Existing semi-supervised learning methods typically handle all pixels in the image equally

ignoring the differences in complexity of different regions within the image. This results in the model’s insufficient learning of challenging regions with higher difficulty to predict

reducing its ability to process challenging areas. Furthermore

since pseudo-labels are generated based on the model’s predictions on unlabeled data

and the model performs poorly in challenging regions

inaccurate predictions increase the risk of introducing noise into the pseudo-labels

thereby reducing their reliability. To address these issues

a semi-supervised learning framework based on suspicious pixels mutual correction (SPMC) is proposed

the framework consists of two networks with identical encoder structures but different upsampling methods in the decoder. Firstly

a common difficulty pixel screening (CDPS) module was designed to handle labeled data. By utilizing suspicious pixels from the prediction results of eatch network to accurately screen out the hard to predict pixels in both networks. These pixels are then corrected using precise supervision signals

thereby improving the model’s prediction accuracy in challenging regions. Secondly

a soft pseudo-label assisted teaching (SPLAT) module was developed to handle unlabeled data. By utilizing trusted pixels from the soft pseudo-labels generated by one network to selectively pseudo-supervise suspicious pixels in the predicted results of the other network. Two networks update parameters through high-quality interaction

thereby reducing the model's cognitive bias and enhancing the quality of the pseudo-labels. Experimental results on three publicly available medical datasets

left atrium (LA)

brain tumor segmentation (BraTS)

and automatic cardiac diagnosis challenge (ACDC)

show that the proposed method is superior to current mainstream semi-supervised learning methods.

关键词

Keywords

references

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