Neural patient-specific 3D-2D registration in laparoscopic liver resection.

PURPOSE: Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. METHODS: The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. RESULTS: The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. CONCLUSION: The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning.

Existing graph analysis techniques generally focus on decreasing the dimensionality of graph data (i.e., removing nodes, edges, or both) in diverse predictive learning tasks in pattern recognition, computer vision, and medical data analysis such as dimensionality reduction, filtering and embedding techniques. However, graph super-resolution is strikingly lacking, i.e., the concept of super-resolving low-resolution (LR) graphs with nr nodes into high-resolution graphs (HR) with [Formula: see text] nodes. Particularly, learning how to automatically generate HR brain connectomes, without resorting to the computationally expensive MRI processing steps such as image registration and parcellation, remains unexplored. To fill this gap, we propose the first technique to super-resolve undirected fully connected graphs with application to brain connectomes. First, we root our brain graph super-resolution (BGSR) framework in learning how to estimate a centered LR population-based brain graph representation, coined as connectional brain template (CBT), acting as a proxy in the target BGSR task. Specifically, we hypothesize that the estimation of a well-representative and centered CBT would help better capture the individuality of each LR brain graph via its residual distance from the population-based CBT. This will eventually allow an accurate identification of the most similar individual graphs to a new testing graph in the LR domain for the target prediction task. Second, we leverage the estimated LR CBT (i.e., population mean) to derive residual LR brain graphs, capturing the deviation of all subjects from the estimated CBT. Third, we learn multi-topology LR graph manifolds using different graph topological measurements (e.g., degree, closeness, betweenness) by estimating residual LR similarity matrices modeling the relationship between pairs of residual LR graphs. These are then fused so we can effectively identify for each testing LR subject its most K similar training LR graphs. Last, the missing testing HR graph is predicted by averaging the HR graphs of the K selected training subjects. Predicted HR from LR functional brain graphs boosted classification results for autistic subjects by 16.48% compared with LR functional graphs.

Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism.

Image-based brain maps, generally coined as 'intensity or image atlases', have led the field of brain mapping in health and disease for decades, while investigating a wide spectrum of neurological disorders. Estimating representative brain atlases constitute a fundamental step in several MRI-based neurological disorder mapping, diagnosis, and prognosis. However, these are strikingly lacking in the field of brain connectomics, where connectional brain atlases derived from functional MRI (fRMI) or diffusion MRI (dMRI) are almost absent. On the other hand, conventional connectomic-based classification methods traditionally resort to feature selection methods to decrease the high-dimensionality of connectomic data for learning how to diagnose new patients. However, these are generally limited by high computational cost and a large variability in performance across different datasets, which might hinder the identification of reproducible biomarkers. To address both limitations, we unprecedentedly propose a brain network atlas-guided feature selection (NAG-FS) method to disentangle the healthy from the disordered connectome. To this aim, given a population of brain connectomes, we propose to learn how estimate a centered and representative functional brain network atlas (i.e., a population center) to reliably map the functional connectome and its variability across training individuals, thereby capturing their shared traits (i.e., connectional fingerprint of a population). Essentially, we first learn the pairwise similarities between connectomes in the population to map them into different subspaces. Next, we non-linearly diffuse and fuse connectomes living in each subspace, respectively. By integrating the produced subspace-specific network atlases we ultimately estimate the population network atlas. Last, we compute the difference between healthy and disordered network atlases to identify the most discriminative features, which are then used to train a predictive learner. Our method boosted the classification performance by 6% in comparison to state-of-the-art FS methods when classifying autistic and healthy subjects.