Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism, and compared our models with current state-ofthe-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73 -100%) for the objective detection of functionally-important white matter pathways including ESM determined eloquent areas such as primary motor, aphasia, speech arrest, auditory, and visual functions. The ?ndings of this study encourage further investigations to determine if DWICNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level, but also the underlying ?bers connecting these cortical areas, to minimize or predict postsurgical functional de?cits. This study translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM, fMRI) are suboptimal. The implementation will be released at https://github. com/HaotianMXu/Brain-?ber-classi?cation-using-CNNs.

Fig. 1.

QuickBundles centroid streamlines of 64 functionally important white matter pathways of interest, C_i, are obtained from the healthy children group. QuickBundles distance threshold [13], [15] was set at 20 mm for each of 64 group-streamline clusters, C_i (n = 70).

Fig. 2.

Shallow CNN (SCNN) architecture for DWI streamline classification.

Fig. 3.

Deep CNN (DCNN) architecture for DWI streamline classification.

Fig. 4.

An example of the attention map of feature maps (with width W and height H) in different channels.

Fig. 5.

Soft attention unit in CNN models.

Fig. 6.

Convergence of training (a) and testing (b) losses in DCNN-CL-ATT obtained from different sample sizes of (training/testing) subjects.

Fig. 7.

Histogram of fiber streamlines in training set, C_i.

Fig. 8.

Confusion matrices of the top four DCNNs which present actual F₁ scores in training data. (a) DCNN-CE (b) DCNN-FL (c) DCNN-CL (d) DCNN-CL-ATT.

Fig. 9.

An example of DCNN-CL-ATT derived-white matter pathway, C₅, for cortical area associated with finger movement of right hand, D₅. Black colored boxes indicate ESM electrodes of D₅ which are spatially well-matched to cortical terminals of C₅ obtained at β = 0.95

Fig. 10.

Comparison of DCNN-CL-ATT derived-white matter pathways C_i with ESM electrode classes D_j. For each functional category of 70 children with a diagnosis of focal epilepsy, voxel-wise overlap count of the ESM electrodes (D_j) was measured in FreeSurfer average template and scaled by its maximum value to estimate overlap probability across subjects in whole bran (left). Similarly, voxel-wise overlap count of corresponding DCNN-CL-ATT classification (C_j) was measured in FreeSurfer average template and scaled by its maximum value to estimate overlap probability across subjects (right).

Fig. 11.

Performance of DCNN-CL-ATT derived-white matter pathways, C_i, to detect ESM electrode classes, D_j, at the group level (n=70). ROC curve analysis was performed as a function of overlap probability (streamline) in C_i in order to evaluate (a) area under curve, (b) sensitivity, and (c) specificity overlapping between all surface vertices of C_i and D_j.

Fig. 12.

Representative examples of DCNN-CL-ATT derived-white matter pathways C_i of which cortical terminals completely overlap D_j. Light green colored clusters indicate ESM class electrodes D_j, spatially well-matched to cortical terminals of the obtained C_i.

Fig. 13.

Representative examples of DCNN-CL-ATT derived-white matter pathways C_i of which cortical terminals incompletely overlap D_j. White dotted circles indicate ESM class electrodes, D₁₇ and D₂₂, spatially illmatched to cortical terminals of the obtained C₂₅ and C₃₉, yielding their low contact probability reported in Table V.

Fig. 14.

Attention maps of representative classes related to primary motor, language, auditory, and visual functions. Higher values are more important for classification. Left: Attention maps for C₁, C₄, C₅, and C₁₆. Right: Attention maps of C₁₁, C₂₁, C₂₄ and C₃₂.