NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging

TL;DR

NodMAISI is a novel nodule-oriented CT synthesis framework that enhances data augmentation, improves lesion detectability, and boosts malignancy classification accuracy in lung cancer screening, especially under limited data conditions.

Contribution

This paper introduces NodMAISI, a new anatomically constrained CT synthesis method that improves synthetic data quality and diagnostic task performance for lung nodule analysis.

Findings

Improved distributional fidelity of synthetic images (FID 1.18-2.99)

Enhanced nodule detectability and sensitivity (e.g., IMD-CT: 0.69 vs 0.39)

Increased malignancy classification AUC by 0.07-0.21 under data scarcity.

Abstract

Objective: Although medical imaging datasets are increasingly available, abnormal and annotation-intensive findings critical to lung cancer screening, particularly small pulmonary nodules, remain underrepresented and inconsistently curated. Methods: We introduce NodMAISI, an anatomically constrained, nodule-oriented CT synthesis and augmentation framework trained on a unified multi-source cohort (7,042 patients, 8,841 CTs, 14,444 nodules). The framework integrates: (i) a standardized curation and annotation pipeline linking each CT with organ masks and nodule-level annotations, (ii) a ControlNet-conditioned rectified-flow generator built on MAISI-v2's foundational blocks to enforce anatomy- and lesion-consistent synthesis, and (iii) lesion-aware augmentation that perturbs nodule masks (controlled shrinkage) while preserving surrounding anatomy to generate paired CT variants. Results: Across six public test datasets, NodMAISI improved distributional fidelity relative to MAISI-v2 (real-to-synthetic FID range 1.18 to 2.99 vs 1.69 to 5.21). In lesion detectability analysis using a MONAI nodule detector, NodMAISI substantially increased average sensitivity and more closely matched clinical scans (IMD-CT: 0.69 vs 0.39; DLCS24: 0.63 vs 0.20), with the largest gains for sub-centimeter nodules where MAISI-v2 frequently failed to reproduce the conditioned lesion. In downstream nodule-level malignancy classification trained on LUNA25 and externally evaluated on LUNA16, LNDbv4, and DLCS24, NodMAISI augmentation improved AUC by 0.07 to 0.11 at <=20% clinical data and by 0.12 to 0.21 at 10%, consistently narrowing the performance gap under data scarcity.