Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation

摘要

Automatic dense parcellation of brain MR image, which labels hundreds of regions of interest (ROIs), plays an important role for neuroimage analysis. Specifically, the brain image parcellation using deep learning technology has been widely recognized for its effective performance, but it remains limited in actual application due to its high demand for sufficient training data and intensive memory allocation of GPU resources. Due to the high cost of manual segmentation, it is usually not feasible to provide large dataset for training the network. On the other hand, it is relatively easy to transfer labeling information to many new unlabeled datasets and thus augment the training data. However, the augmented data can only be considered as weakly labeled for training. Therefore, in this paper, we propose a cascaded weakly super- vised confidence integration network (CINet). The main contributions of our method are two-folds. First, we propose the image registration-based data argumentation method, and evaluate the confidence of the labeling information for each augmented image. The augmented data, as well as the original yet small training dataset, contribute to the modeling of the CINet jointly for segmentation. Second, we propose the random crop strategy to handle the large amount of feature channels in the network, which are needed to label hundreds of neural ROIs. The demanding requirement to GPU memory is thus relieved, while better accuracy can also be achieved. In experiments, we use 37 manually labeled subjects and augment 96 images with weak labels for training. The testing result in overall Dice score over 112 brain regions reaches 75%, which is higher than using the original training data only.