Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation

AbstractThe segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual segmentation cannot be achieved within a sensible time that restricts the usage of accurate quantitative measurement in clinical practice. Therefore, the need for effective automated segmentation techniques is critical. However, a large spatial variability between the structure of brain lesions makes it more challenging. Recently, convolutional neural network (CNN), in particular, the region-based CNN (R-CNN), have attained tremendous progress within the field of object recognition because of its ability to learn and represent features. CNN has proven a last-breaking performance in various fields, such as object recognition, and has also gained more attention in brain imaging, especially in tissue and brain segmentation. In this paper, an automated technique for MS lesion segmentation is proposed, which is built on a 3D patch-wise R-CNN. The proposed system includes two stages: first, segmenting MS lesions in T2-w and FLAIR sequences using R-CNN, then an adaptive neuro-fuzzy inference system (ANFIS) is applied to fuse the results of the two modalities. To evaluate the performance of the proposed method, the public MICCAI2008 MS challenge dataset is employed to segment MS lesions. The experimental results show competitive ...
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research