Abstract

Abstract
The use of optical sensors inside the needle can improve targeting precision and can bring real-time information about the location of the needle tip if necessary, since a needle bends through insertion into the tissue. Therefore, the precise location of the needle tip is so important in percutaneous treatments. In the current experiment, a fiber sensor based on a Fabry-Perot (FP) cavity is described to measure the needle curvature. The sensor is fabricated by producing an air bubble between two sections of multimode fiber. The needle with the sensor therein was attached at one end and deformed by the application of movements. The sensor presents a sensitivity of -0.152 dB/m(-1) to the curvature measurements, with a resolution of 0.089 m(-1). The sensory structure revealed to be stable, obtaining a cross-sensitivity to be 0.03 m(-1)/degrees C.

Abstract
Breast cancer imaging research has seen continuous progress throughout the years. Innovative visualization tools and easier planning techniques are being developed. Image segmentation methodologies generally have best results when applied to specific types of exams or sequences, as their features enhance and expedite those approaches. Particular methods have more purchase with the segmentation of particular structures. This is the case with diverse breast structures and the respective lesions on MRI sequences, over T1w and Dyn. The present study presents a methodology to tackle an unapproached task. We aim to facilitate the volumetric alignment of data retrieved from T1w and Dyn sequences, leveraging breast surface segmentation and registration. The proposed method revolves around Canny edge detection and mending potential holes on the surface, in order to accurately reproduce the breast shape. The contour is refined with a Level-set approach and the surfaces are aligned together using a restriction of the Iterative Closest Point (ICP) method. This could easily be applied to other paired same-time, volumetric sequences. The process seems to have promising results as average two-dimensional contour distances are at sub-voxel resolution and visual results seem well within range for the valid transference of other segmented or annotated structures. © Springer Nature Switzerland AG 2020.

Abstract
Natural teeth eventually fall out as one becomes older, making it more difficult chewing, speaking and get a reference plane for the body postural equilibrium. To minimize the problem, the missing teeth are eventually replaced by implants that restore the referred functions but miss the sensing of the applied force. As a consequence, the masticatory forces become erratic as the brain receives no feedback (or inaccurate) sensing information. The present work aims at developing a preliminary prototype of a smart dental implant meant to restore the proprioceptive control of the masticatory and chewing muscle activity. After the description of the physiological and biomechanical aspects related to tooth loss, details are provided on the force sensing and electrical stimulation provided by the active implant being proposed. Simulation results obtained with the development tool of the GreenPAK programmable chip being used are included.

Abstract
A configuration of a refractometer sensor is described with the aim of optically detecting the crystallization process of paracetamol. The developed sensing head is based on a conventional cleaved multi-mode fiber. The fiber tip sensor structure was submitted to contact with the liquid of interest (paracetamol fully dissolved in 40% v/v of ethanol/water) and the crystallization process of paracetamol, induced with continued exposure to air, was monitored in real time.

Abstract
Epilepsy affects approximately 1% of the world's population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to create automatic and semiautomatic methods for seizure detection and classification to better support patient monitoring management and diagnostic decisions. One of the current promising approaches are the marker-less computer-vision techniques. In this paper an end-to-end deep learning approach is proposed for binary classification of Frontal vs. Temporal Lobe Epilepsies based solely on seizure videos. The system utilizes infrared (IR) videos of the seizures as it is used 24/7 in hospitals' epilepsy monitoring units. The architecture employs transfer learning from large object detection "static" and human action recognition "dynamic" datasets such as ImageNet and Kinectics-400, to extract and classify the clinically known spatiotemporal features of seizures. The developed classification architecture achieves a 5-fold cross-validation f1-score of 0.844 +/- 0.042. This architecture has the potential to support physicians with diagnostic decisions and might be applied for online applications in epilepsy monitoring units. Furthermore, it may be jointly used in the near future with synchronized scene depth 3D information and EEG from the seizures.