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Purpose Focal cortical dysplasias (FCDs) represent one of the most frequent causes of pharmaco-resistant focal epilepsies. Despite improved clinical imaging methods over the past years, FCD detection remains challenging, as FCDs vary in location, size, and shape and commonly blend into surrounding tissues without clear definable boundaries. We developed a novel convolutional neural network for FCD detection and segmentation and validated it prospectively on daily-routine MRIs. Material and methods The neural network was trained on 201 T1 and FLAIR 3 T MRI volume sequences of 158 patients with mainly FCDs, regardless of type, and 7 focal PMG. Non-FCD/PMG MRIs, drawn from 100 normal MRIs and 50 MRIs with non-FCD/PMG pathologies, were added to the training. We applied the algorithm prospectively on 100 consecutive MRIs of patients with focal epilepsy from daily clinical practice. The results were compared with corresponding neuroradiological reports and morphometric MRI analyses evaluated by an experienced epileptologist. Results Best training results reached a sensitivity (recall) of 70.1 % and a precision of 54.3 % for detecting FCDs. Applied on the daily-routine MRIs, 7 out of 9 FCDs were detected and segmented correctly with a sensitivity of 77.8 % and a specificity of 5.5 %. The results of conventional visual analyses were 33.3 % and 94.5 %, respectively (3/9 FCDs detected); the results of morphometric analyses with overall epileptologic evaluation were both 100 % (9/9 FCDs detected) and thus served as reference. Conclusion We developed a 3D convolutional neural network with autoencoder regularization for FCD detection and segmentation. Our algorithm employs the largest FCD training dataset to date with various types of FCDs and some focal PMG. It provided a higher sensitivity in detecting FCDs than conventional visual analyses. Despite its low specificity, the number of false positively predicted lesions per MRI was lower than with morphometric analysis. We consider our algorithm already useful for FCD pre-screening in everyday clinical practice.

Purpose Focal cortical dysplasias (FCD) type IIb and cortical tubers in tuberous sclerosis complex (TSC) are histopathologically similar and are both epileptogenic lesions frequently causing pharmacoresistant epilepsies. Morphometric analysis of T1- and T2-weighted MRI volume data sets can enhance visualization of FCD. Here, we retrospectively investigated whether morphometric MRI analysis is of equal benefit for visualizing cortical tubers. Materials and methods Morphometric analysis was applied to T1- and partly also T2-weighted 1.5T or 3T MRI volume data sets of 15 TSC patients using a fully automated MATLAB ® script (i.e. MAP07) commonly used for FCD detection. In this study, focus was on the most sensitive of the resulting morphometric feature maps (i.e. the ‘junction image') which highlights blurring of the gray–white matter junction in comparison to a normal database. The visualization of tubers in these ‘junction images' was quantitatively compared with that in conventional MR sequences. Results In all patients, morphometric analysis visualized almost all tubers detected in the normal MRI, and additionally highlighted on average 23% (range 3–50%) more tubers which were not detected by visual analysis of the conventional MR sequences. When T2 volume data sets from a 3T scanner were available for postprocessing, the rate of additionally detected tubers increased to 29% on average. These formerly overlooked tubers were usually smaller than the tubers already found in the conventional MRI. Conclusion Morphometric analysis of MRIs in TSC can highlight cortical tubers which are likely to be overlooked in conventional MRI sequences alone. Additionally detected tubers may be of potential importance for both presurgical evaluation and initial diagnosis of TSC.

Summary Purpose Focal cortical dysplasias (FCD) are highly epileptogenic lesions frequently accounting for pharmaco-resistant focal epilepsy. Visual MRI analysis combined with morphometric analysis of T1-weighted MRI data was shown to be of higher diagnostic sensitivity in detecting and delineating FCD than conventional visual analysis alone. Here we investigate whether morphometric analysis of T2-weighted MRI volume data sets is of equal benefit or perhaps more helpful for visualizing FCD. Materials and methods Morphometric analysis was applied to T1- and T2-weighted MRI volume data sets of 20 epilepsy patients with FCD using a fully automated MATLAB script with scanner- and sequence-specific normal databases for T1 and T2 images. For each modality, a new feature map (i.e., ‘junction image') highlighting the FCD-typical blurring of the gray–white matter junction and quantifying this feature in comparison to the normal database in terms of z -scores was calculated. The resulting T1 and T2 ‘junction images' were compared for conspicuity and recognizability of the FCD both qualitatively by visual assessment and quantitatively by analysis of the mean z -scores inside and outside the lesions. Results In 80% of the cases, the FCD presented with higher contrast and/or clearer delineation in the T2 than in the T1 ‘junction images' and were thus easier to recognize in these images. The quantitative analysis supported this impression: in 95% of cases, the ratio of mean z -scores inside and outside the FCD was higher in T2- than in T1-based ‘junction images'. For the T2 ‘junction images', this ratio amounted to 8.7 on average and was thus more than twice as high as the corresponding T1 result of 3.7 ( p Conclusion Concerning visualization of FCD by highlighting blurring of the gray–white matter junction, the results of the present study indicate that morphometric analysis of T2-weighted MRI data on average is superior to T1-based morphometry.