Confidence-aware self-supervised learning for dense monocular depth estimation in dynamic laparoscopic scene.

This paper tackles the challenge of accurate depth estimation from monocular laparoscopic images in dynamic surgical environments. The lack of reliable ground truth due to inconsistencies within these images makes this a complex task. Further complicating the learning process is the presence of noise elements like bleeding and smoke.

Complex-Valued Wavelet Spectrum Analysis of Respiratory Conditions and Its Feasibility in the Detection of Low-Functional Respiration.

Respiratory monitoring is a significant issue to reduce patient risks and medical staff labor in postoperative care and epidemic infection, particularly after the COVID-19 pandemic. Oximetry is widely used for respiration monitoring in the clinic, but it sometimes fails to capture a low-functional respiratory condition even though a patient has breathing difficulty. Another approach is breathing-sound monitoring, but this is unstable due to the indirect measurement of lung volume.

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks.

Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem.

A Small and High-Speed Driving Mechanism for 3D Shape Measurement in Monocular Endoscopy.

Three-dimensional (3D) shape acquisition has been widely introduced to enrich quantitative analysis with the combination of object shape and texture, for example, surface roughness evaluation in industry and gastrointestinal endoscopy in medicine. Shape from focus is a promising technique to measure substance surfaces in 3D space because no occlusion problem appears in principle, as does with stereo shape measurement, which is another commonly used option. We have been developing endoscopic shape measurement devices and shape reconstruction algorithms.

Computational reproductions of external force field adaption without assuming desired trajectories.

Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements.

Label cleaning and propagation for improved segmentation performance using fully convolutional networks.

Purpose: In recent years, fully convolutional networks (FCNs) have been applied to various medical image segmentation tasks. However, it is difficult to generate a large amount of high-quality annotation data to train FCNs for medical image segmentation. Thus, it is desired to achieve high segmentation performances even from incomplete training data.

Suction-fixing surgical device for assisting liver manipulation with laparoscopic forceps.

Purpose: Noninvasiveness and stability are significant issues in laparoscopic liver resection. Inappropriate grasping force can cause damage or serious bleeding to the liver. In addition, instability of grasping can result unsafe operations or wavered cutting.

When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance.

Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to an accuracy level defined heuristically, often set to 90% of the variance. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is not entirely random and can influence the execution of motor tasks.

Three-dimensional posture estimation of robot forceps using endoscope with convolutional neural network.

Background: In recent years, there has been significant developments in surgical robots. Image-based sensing of surgical instruments, without the use of electric sensors, are preferred for easily washable robots.

Methods: We propose a method to estimate the three-dimensional posture of the tip of the forceps tip by using an endoscopic image.

Body ownership and agency altered by an electromyographically controlled robotic arm.

Understanding how we consciously experience our bodies is a fundamental issue in cognitive neuroscience. Two fundamental components of this are the sense of body ownership (the experience of the body as one's own) and the sense of agency (the feeling of control over one's bodily actions). These constructs have been used to investigate the incorporation of prostheses.

Incorporation of prosthetic limbs into the body representation of amputees: Evidence from the crossed hands temporal order illusion.

Understanding how we consciously experience our bodies is a fundamental issue in both psychology and neuroscience. To date, the incorporation of nonbody objects into the body representation has been investigated extensively, and the incorporation of prosthetic arms in amputees has been demonstrated using the rubber hand illusion. In this study, we investigated the incorporation of prosthetic arms in amputees using the crossed hands illusion, in which successive somatosensory stimuli are delivered, one to each arm, at intervals of 300ms or less, and where arm crossing often causes inversion of perceived tactile temporal order.

Affective Stimuli for an Auditory P300 Brain-Computer Interface.

Gaze-independent brain computer interfaces (BCIs) are a potential communication tool for persons with paralysis. This study applies affective auditory stimuli to investigate their effects using a P300 BCI. Fifteen able-bodied participants operated the P300 BCI, with positive and negative affective sounds (PA: a meowing cat sound, NA: a screaming cat sound).

Decoding finger movement in humans using synergy of EEG cortical current signals.

The synchronized activity of neuronal populations across multiple distant brain areas may reflect coordinated interactions of large-scale brain networks. Currently, there is no established method to investigate the temporal transitions between these large-scale networks that would allow, for example, to decode finger movements. Here we applied a matrix factorization method employing principal component and temporal independent component analyses to identify brain activity synchronizations.

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.

Objective: Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles.

Feasibility of magnetic resonance elastography for the pancreas at 3T.

Purpose: 1) To assess the usefulness of an elastic belt bracing the upper abdomen for reducing the miscalculated areas of the pancreas on 3.0T magnetic resonance elastography (MRE); 2) to test whether MRE can detect difference of stiffness between normal pancreas and the focal pancreatic diseases.

Materials And Methods: Using an initial eight normal volunteers, miscalculated areas were compared between MRE with the elastic belt and without the belt on 3.

Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI.

Objective: This study presents a new steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) using flickering visual stimuli at frequencies greater than the critical flicker frequency (CFF).

Methods: We first asked participants to fixate on a green/blue flicker (30-70Hz), and SSVEP amplitude was evaluated. Participants were asked to indicate whether the stimulus was visibly flickering and to report their subjective level of discomfort.

Implementation of a beam forming technique in real-time magnetoencephalography.

Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience. In the present study, we implemented voxel-based real-time coherence measurements in a rtMEG system in which we employed a beamformer to localize signal sources in the anatomical space prior to computing imaginary coherence. Our rtMEG experiment showed that a healthy subject could increase coherence between the parietal cortex and visual cortex when attending to a flickering visual stimulus.

A BMI-based occupational therapy assist suit: asynchronous control by SSVEP.

A brain-machine interface (BMI) is an interface technology that uses neurophysiological signals from the brain to control external machines. Recent invasive BMI technologies have succeeded in the asynchronous control of robot arms for a useful series of actions, such as reaching and grasping. In this study, we developed non-invasive BMI technologies aiming to make such useful movements using the subject's own hands by preparing a BMI-based occupational therapy assist suit (BOTAS).

The effect of temporal perception on weight perception.

A successful catch of a falling ball requires an accurate estimation of the timing for when the ball hits the hand. In a previous experiment in which participants performed ball-catching task in virtual reality environment, we accidentally found that the weight of a falling ball was perceived differently when the timing of ball load force to the hand was shifted from the timing expected from visual information. Although it is well known that spatial information of an object, such as size, can easily deceive our perception of its heaviness, the relationship between temporal information and perceived heaviness is still not clear.