Detection of acute cerebral hemorrhage in rabbits by magnetic induction

Acute cerebral hemorrhage (ACH) is an important clinical problem that is often monitored and studied with expensive devices such as computed tomography, magnetic resonance imaging, and positron emission tomography. These devices are not readily available in economically underdeveloped regions of the world, emergency departments, and emergency zones. We have developed a less expensive tool for non-contact monitoring of ACH. The system measures the magnetic induction phase shift (MIPS) between the electromagnetic signals on two coils. ACH was induced in 6 experimental rabbits and edema was induced in 4 control rabbits by stereotactic methods, and their intracranial pressure and heart rate were monitored for 1 h. Signals were continuously monitored for up to 1 h at an exciting frequency of 10.7 MHz. Autologous blood was administered to the experimental group, and saline to the control group (1 to 3 mL) by injection of 1-mL every 5 min. The results showed a significant increase in MIPS as a function of the injection volume, but the heart rate was stable. In the experimental (ACH) group, there was a statistically significant positive correlation of the intracranial pressure and MIPS. The change of MIPS was greater in the ACH group than in the control group. This high-sensitivity system could detect a 1-mL change in blood volume. The MIPS was significantly related to the intracranial pressure. This observation suggests that the method could be valuable for detecting early warning signs in emergency medicine and critical care units

Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation Under Hand-Object Interaction

We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS???19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS???19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27 mm to 13 mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones