RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features

Autor:	Na, Inye, Kim, Jonghun, Ko, Eun Sook, Park, Hyunjin
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor images. Characterized by its low-dimensional yet biologically meaningful markers, radiomics bridges the gap between complex medical imaging data and actionable clinical insights. We present RadiomicsFill-Mammo, the first of the RadiomicsFill series, an innovative technique that generates realistic mammogram mass images mirroring specific radiomics attributes using masked images and opposite breast images, leveraging a recent stable diffusion model. This approach also allows for the incorporation of essential clinical variables, such as BI-RADS and breast density, alongside radiomics features as conditions for mass generation. Results indicate that RadiomicsFill-Mammo effectively generates diverse and realistic tumor images based on various radiomics conditions. Results also demonstrate a significant improvement in mass detection capabilities, leveraging RadiomicsFill-Mammo as a strategy to generate simulated samples. Furthermore, RadiomicsFill-Mammo not only advances medical imaging research but also opens new avenues for enhancing treatment planning and tumor simulation. Our code is available at https://github.com/nainye/RadiomicsFill. Comment: Accepted at MICCAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.05683 Zobrazit plný text záznamu View this record from Arxiv