Convolutional neural networks (ConvNets) have quickly become the most widely used tool for image perception and interpretation tasks over the past several years. The single most important resource needed for training a ConvNet that will successfully generalize to unseen examples is an adequately sized labeled dataset. In many interesting medical imaging cases, the necessary size or quality of training data is not suitable for directly training a ConvNet. Furthermore, access to the expertise to manually label such datasets is often infeasible. To address these barriers, we investigate a method for iterative refinement of the ConvNet training. Initially, unlabeled images are attained, minimal labeling is performed, and a model is trained on the sparse manual labels. At the end of each training iteration, full images are predicted, and additional manual labels are identified to improve the training dataset.
In this work, we show how to utilize patch-based ConvNets to iteratively build a training dataset for automatically segmenting MRI images of the human cerebellum. We construct this training dataset using a small collection of high-resolution 3D images and transfer the resulting model to a much larger, much lower resolution, collection of images. Both T1-weighted and T2-weighted MRI modalities are utilized to capture the additional features that arise from the differences in contrast between modalities. The objective is to perform tissue-level segmentation, classifying each volumetric pixel (voxel) in an image as white matter, gray matter, or cerebrospinal fluid (CSF). We will present performance results on the lower resolution dataset, and report achieving a 12.7% improvement in accuracy over the existing segmentation method, expectation maximization. Further, we will present example segmentations from our iterative approach that demonstrate it’s ability to detect white matter branching near the outer regions of the anatomy, which agrees with the known biological structure of the cerebellum and has typically eluded traditional segmentation algorithms.