Confidence-Enhanced Semi-supervised Learning for Mediastinal Neoplasm Segmentation1

Abstract

AbstractAutomated segmentation of mediastinal neoplasms in preoperative computed tomography (CT) scans is critical for accurate diagnosis. Though convolutional neural networks (CNNs) have proven effective in medical imaging analysis, the segmentation of mediastinal neoplasms, which vary greatly in shape, size, and texture, presents a unique challenge due to the inherent local focus of convolution operations. To address this limitation, we propose a confidence-enhanced semi-supervised learning framework for mediastinal neoplasm segmentation. Specifically, we introduce a confidence-enhanced module that improves segmentation accuracy over indistinct tumor boundaries by assessing and excluding unreliable predictions simultaneously, which can greatly enhance the efficiency of exploiting unlabeled data. Additionally, we implement an iterative learning strategy designed to continuously refine the estimates of prediction reliability throughout the training process, ensuring more precise confidence assessments. Quantitative analysis on a real-world dataset demonstrates that our model significantly improves the performance by leveraging unlabeled data, surpassing existing semi-supervised segmentation benchmarks. Finally, to promote more efficient academic communication, the analysis code is publicly available athttps://github.com/fxiaotong432/CEDS.Author summaryIn clinical practice, computed tomography (CT) scans can aid in the detection and evaluation of mediastinal tumors. The early detection of mediastinal tumors plays a crucial role in formulating appropriate treatment plans and improving patient survival rates. To reduce the high cost of manual annotation, researchers have attempted to employ convolutional neural networks (CNNs) for efficient automatic segmentation. However, the significant challenges arise due to the considerable variation in shape, size, and texture of mediastinal tumors, posing difficulties for the segmentation task. In this study, we introduce a confidence-enhanced module with a semi-supervised learning framework. By evaluating the model’s prediction confidence and selecting high-confidence predictions, we improve the efficiency and quality of data utilization. This approach demonstrates the achievement of accurate mediastinal tumor segmentation with only a minimal amount of labeled data. Our research not only provides an effective technical approach for automatic segmentation of mediastinal tumors but also opens up new possibilities for optimizing strategies in semi-supervised learning methods.