Synthetic Lung Nodule 3D Image Generation Using Autoencoders

Computer aided diagnosis, where a software tool analyzes the patient's medical imaging results to suggest a possible diagnosis, is a promising direction: froman input low-resolution 3D CT scan, image processing techniques can be used to classify nodules in the lung scan as potentially cancerous or benign. But such systems require quality 3D training images to ensure the classifiers are adequately trained with sufficient generality. Cancerous lung nodule detection still suffers from a dearth of training images which hampers the ability to effectively automate and improve the analysis of CT scans for cancer risks (Valente et al., 2016). In this work, we propose to address this problem by automatically generating synthetic 3D images of nodules, to augment the training dataset of such systems with meaningful (yet computer-generated) lung nodules images. This is the full length paper for work originally presentedat the 3rd International Workshop on Biomedical Informatics with Optimization and Machine Learning in conjuction with International Joint Conference on Artificial Intelligence (IJCAI) (Kommrusch & Pouchet, 2018). Li et al. showed how to analyze nodules using computed features from the 3D images (such as volume, degree of compactness and irregularity, etc.) (Q.