Enhancement of Semantic Segmentation by Image-Level Fine-Tuning to Overcome Image Pattern Imbalance in HRCT of Diffuse Infiltrative Lung Diseases

Diagnosing diffuse infiltrative lung diseases (DILD) using high-resolution computed tomography (HRCT) is challenging, even for expert radiologists, due to the complex and variable image patterns. Moreover, the imbalances among the six key DILD-related patterns—normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation—further complicate accurate segmentation and diagnosis. This study presents an enhanced U-Net-based segmentation technique aimed at addressing these challenges. The primary contribution of our work is the fine-tuning of the U-Net model using image-level labels from 92 HRCT images that include various types of DILDs, such as cryptogenic organizing pneumonia, usual interstitial pneumonia, and nonspecific interstitial pneumonia. This approach helps to correct the imbalance among image patterns, improving the model's ability to accurately differentiate between them. By employing semantic lung segmentation and patch-level machine learning, the fine-tuned model demonstrated improved agreement with radiologists' evaluations compared to conventional methods. This suggests a significant enhancement in both segmentation accuracy and inter-observer consistency. In conclusion, the fine-tuned U-Net model offers a more reliable tool for HRCT image segmentation, making it a valuable imaging biomarker for guiding treatment decisions in patients with DILD. By addressing the issue of pattern imbalances, our model significantly improves the accuracy of DILD diagnosis, which is crucial for effective patient care.