Architecture of the fully connected CNN with a 3 layer “U-Net” architecture.

<p>Architecture of the fully connected CNN with a 3 layer “U-Net” architecture.</p

Segmentation examples with for each example: Original resized slices ¹⁸F-FET (top), original mask (middle up), predicted mask + threshold (middle down) and difference between the predicted and original mask (bottom, with false negative in red and false positive in blue).

<p>Segmentation examples with for each example: Original resized slices ¹⁸F-FET (top), original mask (middle up), predicted mask + threshold (middle down) and difference between the predicted and original mask (bottom, with false negative in red and false positive in blue).</p

Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).

<p>Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).</p

Confusion matrix at the voxel level in the validation set of 11 patients.

<p>Confusion matrix at the voxel level in the validation set of 11 patients.</p

Box plots of the dot multiplication in training and testing populations.

<p>Box plots of the dot multiplication in training and testing populations.</p

Cerebral ¹⁸F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach

<div><p>Introduction</p><p>Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF ¹⁸F-FDG brain profiles.</p><p>Methods</p><p>¹⁸F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated.</p><p>Results</p><p>The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%.</p><p>Conclusion</p><p>We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using ¹⁸F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.</p></div

Comparison between ¹⁸F-FDG PET brain images of 100 MMF patients and 44 healthy subjects included in the training set.

<p>Brain areas with significant decreased uptake of ¹⁸F-FDG served as mask to train the support vector machine classifier. Results were collected at a P-value < 0.005 at the voxel level, for clusters k ≥ 200 voxels with adjustment for age.</p

Statistical parametric mapping (SPM) and support vector machine (SVM) procedures.

<p>MMF, Macrophagic myofasciitis; L, left; R, right.</p

Population characteristics for the training and testing groups.

<p>Population characteristics for the training and testing groups.</p

Confusion matrix of the result of the support vector machine classifier for the diagnosis.

<p>Confusion matrix of the result of the support vector machine classifier for the diagnosis.</p