Generation of Chest CT pulmonary Nodule Images by Latent Diffusion Models using the LIDC-IDRI Dataset

2026-01-16

AI Summary

This study addresses the scarcity of specific types of pulmonary nodule CT images in clinical practice, which leads to data insufficiency and class imbalance in computer-aided diagnosis systems. Leveraging the LIDC-IDRI dataset, the authors construct paired data comprising nodule images and corresponding radiologist annotations, and for the first time apply latent diffusion models (Stable Diffusion v1.5/v2.0) guided by clinical text prompts to synthesize CT images exhibiting targeted diagnostic characteristics. Experimental results demonstrate that images generated with Stable Diffusion v2 at a guidance scale (GS) of 5 achieve optimal balance among image quality, diversity, and textual fidelity. Subjective evaluation further indicates no statistically significant perceptual difference between the synthesized and real clinical images, thereby offering an effective solution to the challenge of medical image scarcity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Recently, computer-aided diagnosis systems have been developed to support diagnosis, but their performance depends heavily on the quality and quantity of training data. However, in clinical practice, it is difficult to collect the large amount of CT images for specific cases, such as small cell carcinoma with low epidemiological incidence or benign tumors that are difficult to distinguish from malignant ones. This leads to the challenge of data imbalance. In this study, to address this issue, we proposed a method to automatically generate chest CT nodule images that capture target features using latent diffusion models (LDM) and verified its effectiveness. Using the LIDC-IDRI dataset, we created pairs of nodule images and finding-based text prompts based on physician evaluations. For the image generation models, we used Stable Diffusion version 1.5 (SDv1) and 2.0 (SDv2), which are types of LDM. Each model was fine-tuned using the created dataset. During the generation process, we adjusted the guidance scale (GS), which indicates the fidelity to the input text. Both quantitative and subjective evaluations showed that SDv2 (GS = 5) achieved the best performance in terms of image quality, diversity, and text consistency. In the subjective evaluation, no statistically significant differences were observed between the generated images and real images, confirming that the quality was equivalent to real clinical images. We proposed a method for generating chest CT nodule images based on input text using LDM. Evaluation results demonstrated that the proposed method could generate high-quality images that successfully capture specific medical features.

Problem

• data imbalance
• chest CT
• pulmonary nodule
• medical image generation
• training data scarcity

Innovation

• Latent Diffusion Models
• Medical Image Synthesis
• Text-to-Image Generation
• Chest CT
• Data Augmentation