Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
This addresses the problem of medical image segmentation for researchers and clinicians in scenarios with small datasets, though it is incremental as it builds on existing methods like adversarial defense and task reorganization.
The paper tackles brain MR image segmentation with limited training data by using adversarial defense for data augmentation and hierarchical task reorganization, achieving an 84.46% Dice score on a test set with only seven training subjects.
Medical image segmentation is challenging especially in dealing with small dataset of 3D MR images. Encoding the variation of brain anatomical struc-tures from individual subjects cannot be easily achieved, which is further chal-lenged by only a limited number of well labeled subjects for training. In this study, we aim to address the issue of brain MR image segmentation in small da-taset. First, concerning the limited number of training images, we adopt adver-sarial defense to augment the training data and therefore increase the robustness of the network. Second, inspired by the prior knowledge of neural anatomies, we reorganize the segmentation tasks of different regions into several groups in a hierarchical way. Third, the task reorganization extends to the semantic level, as we incorporate an additional object-level classification task to contribute high-order visual features toward the pixel-level segmentation task. In experiments we validate our method by segmenting gray matter, white matter, and several major regions on a challenge dataset. The proposed method with only seven subjects for training can achieve 84.46% of Dice score in the onsite test set.