Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

TL;DR

This paper proposes a novel semi-supervised tumor segmentation method using iterative pseudo-labeling and adaptive copy-paste augmentation, effectively handling small tumors and leveraging data augmentation strategies.

Contribution

Introduces IPA-CP, a semi-supervised framework with uncertainty-based adaptive augmentation and iterative pseudo-labeling for improved tumor segmentation.

Findings

Outperforms state-of-the-art SSL methods on multiple datasets

Effective in segmenting small and challenging tumors

Ablation studies confirm the technical contributions' effectiveness

Abstract

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.