Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition

TL;DR

This paper introduces a framework for synthesizing manipulable 3D medical objects, like lung nodules, to augment training data and improve detection performance in medical imaging tasks.

Contribution

We propose a novel method for decomposing and synthesizing 3D objects in medical images with controllable properties, enhancing data augmentation for better model generalization.

Findings

Generated realistic, manipulable lung nodules in 3D CT images.

Synthetic data improved nodule detection performance by 8.44%.

Framework enables shape, texture, and location control of synthetic objects.

Abstract

The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations. Such systems require data to be annotated by experts with years of training, especially when diagnostic decisions are involved. Such datasets are thus hard to scale up. In this context, it is hard for supervised learning systems to generalize to the cases that are rare in the training set but would be present in real-world clinical practices. We believe that the synthetic image samples generated by a system trained on the real data can be useful for improving the supervised learning tasks in the medical image analysis applications. Allowing the image synthesis to be manipulable could help synthetic images provide complementary information to the training data rather than simply duplicating the real-data manifold. In this paper, we propose a framework for synthesizing 3D objects, such as pulmonary nodules, in 3D medical images with manipulable properties. The manipulation is enabled by decomposing of the object of interests into its segmentation mask and a 1D vector containing the residual information. The synthetic object is refined and blended into the image context with two adversarial discriminators. We evaluate the proposed framework on lung nodules in 3D chest CT images and show that the proposed framework could generate realistic nodules with manipulable shapes, textures and locations, etc. By sampling from both the synthetic nodules and the real nodules from 2800 3D CT volumes during the classifier training, we show the synthetic patches could improve the overall nodule detection performance by average 8.44% competition performance metric (CPM) score.