Automatic cortical target point localisation in MRI for transcranial magnetic stimulation via a multi-resolution convolutional neural network.

PURPOSE: Transcranial magnetic stimulation (TMS) is a growing therapy for a variety of psychiatric and neurological disorders that arise from or are modulated by cortical regions of the brain represented by singular 3D target points. These target points are often determined manually with assistance from a pre-operative T1-weighted MRI, although there is growing interest in automatic target point localisation using an atlas. However, both approaches can be time-consuming which has an effect on the clinical workflow, and the latter does not take into account patient variability such as the varying number of cortical gyri where these targets are located. METHODS: This paper proposes a multi-resolution convolutional neural network for point localisation in MR images for a priori defined points in increasingly finely resolved versions of the input image. This approach is both fast and highly memory efficient, allowing it to run in high-throughput centres, and has the capability of distinguishing between patients with high levels of anatomical variability. RESULTS: Preliminary experiments have found the accuracy of this network to be [Formula: see text] mm, compared to [Formula: see text] mm for deformable registration and [Formula: see text] mm for a human expert. For most treatment points, the human expert and proposed CNN statistically significantly outperform registration, but neither statistically significantly outperforms the other, suggesting that the proposed network has human-level performance. CONCLUSIONS: The human-level performance of this network indicates that it can improve TMS planning by automatically localising target points in seconds, avoiding more time-consuming registration or manual point localisation processes. This is particularly beneficial for out-of-hospital centres with limited computational resources where TMS is increasingly being administered.

SepaConvNet for Localizing the Subthalamic Nucleus Using One Second Micro-electrode Recordings.

Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Development of an ELISA to detect clenbuterol in swine products using a new approach for hapten design.

This research outlines the application of an enzyme-linked immunosorbent assay (ELISA) for the analysis of clenbuterol in animal products. Our assay showed good sensitivity for clenbuterol (0.4 ng/g or 0.4 ppb) and low detection limit (0.09 ng/g or 0.09 ppb). A low cross-reactivity for other ß2-agonist drugs such as salbutamol, terbutaline, and epinephrine led to formatting an ELISA kit considered to have a high specificity for clenbuterol. A survey of Ho Chi Minh City pork market was conducted as part of the validation of our ELISA. ELISA results showed a surprisingly high value of contamination. However, it will be necessary to conduct a more statistically valid replicated survey with evaluation by other instrumental methods to obtain a definite conclusion. This ELISA kit will be used to monitor growth promoter residues in Vietnam's animal products.