Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation

• URL: http://arxiv.org/abs/2410.12542v1
• Date: Wed, 16 Oct 2024 13:20:57 GMT
• Title: Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
• Authors: Kathrin Khadra, Utku Türkbey,
• Abstract summary: This work proposes a memory-efficient patch-wise denoising diffusion probabilistic model (DDPM) for generating synthetic medical images. Our approach generates high-utility synthetic images with nodule segmentation while efficiently managing memory constraints. We evaluate the method in two scenarios: training a segmentation model exclusively on synthetic data, and augmenting real-world training data with synthetic images.
• Score: 0.0
• License:
• Abstract: The scarcity of publicly available medical imaging data limits the development of effective AI models. This work proposes a memory-efficient patch-wise denoising diffusion probabilistic model (DDPM) for generating synthetic medical images, focusing on CT scans with lung nodules. Our approach generates high-utility synthetic images with nodule segmentation while efficiently managing memory constraints, enabling the creation of training datasets. We evaluate the method in two scenarios: training a segmentation model exclusively on synthetic data, and augmenting real-world training data with synthetic images. In the first case, models trained solely on synthetic data achieve Dice scores comparable to those trained on real-world data benchmarks. In the second case, augmenting real-world data with synthetic images significantly improves segmentation performance. The generated images demonstrate their potential to enhance medical image datasets in scenarios with limited real-world data.

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