Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

TL;DR

This paper introduces a local contrastive loss leveraging pseudo-labels for semi-supervised medical image segmentation, improving pixel-level feature learning by combining limited labeled data with unlabeled data.

Contribution

It proposes a novel local contrastive loss that uses pseudo-labels to enhance pixel-level feature learning in semi-supervised segmentation tasks.

Findings

Achieved high segmentation accuracy on three public datasets.

Effectively utilized unlabeled data with pseudo-labels for improved learning.

Outperformed existing semi-supervised segmentation methods.

Abstract

Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet. In pixel-level prediction tasks such as segmentation, it is crucial to also learn good local level representations along with global representations to achieve better accuracy. However, the impact of the existing local contrastive loss-based methods remains limited for learning good local representations because similar and dissimilar local regions are defined based on random augmentations and spatial proximity; not based on the semantic label of local regions due to lack of large-scale expert annotations in the semi/self-supervised setting. In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images. In particular, we define the proposed loss to encourage similar representations for the pixels that have the same pseudo-label/ label while being dissimilar to the representation of pixels with different pseudo-label/label in the dataset. We perform pseudo-label based self-training and train the network by jointly optimizing the proposed contrastive loss on both labeled and unlabeled sets and segmentation loss on only the limited labeled set. We evaluated on three public cardiac and prostate datasets, and obtain high segmentation performance.