Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting
This addresses the need for efficient and accurate tumor segmentation in medical imaging to aid surgical planning, though it is incremental as it builds on existing inpainting and anomaly detection methods.
The paper tackles the problem of automatic brain tumor segmentation in MRI without manual annotations by using unsupervised adversarial image inpainting to reconstruct healthy brain regions and identify anomalies based on reconstruction loss, achieving a mean Dice score of 0.771 with a standard deviation of 0.176.
Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively.