An Unsupervised Suggestive Annotation Algorithm for 3D CT Image Processing

It is necessary to interactively annotate a set of training data when applying deep learning technology on 3D CT images.This requires a huge amount of workload by medical experts for manual annotations.This paper proposes an unsupervised suggestive annotation algorithm for 3D CT images that employs two new techniques(densely connected deep auto encoder and density-spectral clustering)to significantly reduce annotation requirements.Our algorithm results in three advantages.First,it is fully unsupervised.Second,a new auto-encoder named DCDAE is proposed to reduce the amount of model parameters and extract discriminative features by combining deep autoencoder and dense connection structure.Third,a new clustering algorithm named density-spectral clustering is proposed to find the outliers and automatically adjust the cluster number according to the affinity matrix of the dataset.The algorithm is applied on lung nodule semantic segmentation task using LIDC-IDRI dataset.