Fig 1.
Example of three different epileptogenic lesions highlighted in red.
A: (patient #4) coronal slice showing a hippocampus anomaly, B: (patient #1) axial slice showing signal and texture change, and C: (patient #2) sagittal slice showing a deep sulcus.
Fig 2.
Scheme of the CAD system illustrating the learning (top) and the testing phase (bottom).
Fig 3.
Example slice of: (A) GM probability map (B) WM probability map (C) CSF probability map (D) extension map (E) junction map.
Fig 4.
Example of OC-SVM score histogram (on a log-scale) obtained for a control subject from NDB1 (blue) overlaid with that of patient #1 (red).
There are differences in a small number of voxels, all with a negative signed distance to the hyperplanes indicating non-normal tissue.
Fig 5.
(A) Example slice of the original MRI where the alteration location is highlighted in blue, (B) zoom on the original MRI before introducing the alteration, (C) and zoom on the introduced junction alteration. The introduced lesion has a very low contrast and is almost impossible to detect with the naked eye. (D) Example of a simulation subject sagittal MRI slice showing two heterotopion-like lesions (within the green circles) that were simulated using GM values selected within the range I of grey-level values.
Fig 6.
Hyper-parameter optimization curve.
The bigger the width of the RBF kernel, the worse the generalisability due to the risk of under-fitting. Similarly, the smaller the value of ν, the higher the risk of over-fitting (fewer observations may be excluded); for better generalisability and given noise in medical images, the value should not be too small. Here, the pair (ν = 0.03, σ = 4) is therefore the optimal combination.
Fig 7.
Comparison of OC-SVM and SPM performance for the simulated blurred junction and heterotopion-like lesions.
Table 1.
Comparison of OC-SVM and SPM classification performance.
Data are differences in AUCs, with 95% confidence intervals in brackets. All differences are significant and in favour of OC-SVM, except for the detection of the blurred junction where no difference between the techniques can be shown.
Fig 8.
Example of OC-SVM and SPM labelled cluster maps for the blurred junction simulation.
Table 2.
OC-SVM and SPM classification results for clinical data for a p-value of 0.001.
The third column indicates the location of the epileptogenic lesions reported by the clinician for each patient. Columns 4 to 7 report the detection results of OC-SVM and the three SPM analyses. For each method, the number of false positive clusters is indicated in parentheses. The ⋆ in column 2 indicates the FCD lesions that were confirmed by histology.
Fig 9.
Example MIP of the detected cluster maps (blue) for patient #2 (MRI+) overlaid on the MIP of the expert delineated lesion (red).
(a) OC-SVM distance map thresholded at p < 0.001; (b) SPM analysis based on the T-score map from the conjunction of both contrasts thresholded at p < 0.001 (c) SPM junction-based T-score map thresholded at p < 0.001; (d) SPM extension-based T-score map thresholded at p < 0.001.
Fig 10.
Example MIP of the detected cluster maps (blue) for patient #10 (MRI-), the presumed lesion is indicated with the yellow arrow.
(a) OC-SVM distance map thresholded at p < 0.001; (b) SPM analysis based on the T-score map from the conjunction of both contrasts thresholded at p < 0.001 (c) SPM junction-based T-score map thresholded at p < 0.001; (d) SPM extension-based T-score map thresholded at p < 0.001.