Automatic localization and segmentation of focal cortical dysplasia in FLAIR-negative patients using a convolutional neural network.

PURPOSE

Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid-attenuated inversion recovery (FLAIR)-negative lesions using convolutional neural network (CNN) technology.

METHODS

The technique involves training a six-layer CNN named Net-Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net-Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR-negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm.

RESULTS

The PIBs most similar to an FCD lesion image block were identified by the trained Net-Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR-negative lesion images from 12 patients. The subject-wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68.

CONCLUSION

We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR-negative FCD lesions. This work is the first study to apply a CNN-based model to detect and segment FCD lesions in images of FLAIR-negative lesions.