Miracle-dz/UniFAR-FTUs huggingface dataset:
Teleoperated Probe Manipulator for Prone-Position Echocardiography Examination

Echocardiography probe manipulation is a strenuous task. During a procedure, the operator must hold the probe, extend their arm, bend their elbow, and monitor the resulting image simultaneously, which causes strain and introduces variability to the measurement. We propose a teleoperated probe manipulation robot to reduce the burden of handling the probe and minimize the infection risk during the COVID pandemic. The proposed robot utilizes prone position scanning that could enlarge the cardiac windows for easier scanning and eliminate the risk of the robot pressing down on the patient. We derived the robot's requirements based on a real clinical scenario. Initial evaluation showed that the robot could achieve the required range of motion, force, and control. The robot's functionality was tested by a non-clinician, in which the tester could obtain heart images of a volunteer in under one minute.
Electrophysiological differences in distinct hearing threshold level individuals with and without tinnitus distress

Tinnitus is the perception of sound when no actual external noise is present. Tinnitus is highly prevalent, with more than 1 in 7 adults in the EU having tinnitus, and it causes negative effects on quality of life for many individuals. However, there is currently no cure for tinnitus and its pathophysiology and genesis are unknown. Auditory evoked potentials (AEPs) provide a non-invasive means by which the electrical signals evoked by the brain can be recorded, and constitute a useful indicator for the evaluation of auditory disorders such as tinnitus and hearing loss. The present study analyzed a total of 98 auditory middle evoked potential (AMLR) waveforms, a subtype of AEPs, from 49 participants with subjective tinnitus, attempting to identify differences in AMLR parameters between sufferers with and without tinnitus distress. The waveforms were divided into three categories according to the ear's hearing level, and comparisons were made between sufferers in the same hearing level category. The results of the analysis indicated some statistically significant differences in AMLR latencies and amplitudes between the compared groups. Clinical Relevance– Identification of the electro-physiological profile of subjective tinnitus sufferers based on the distress manifested by tinnitus using AMLRs.
Force Controlled Deburring using a Collaborative Robot

This paper proposes a novel approach to robotic deburring. Although most of the methods already presented use a spindle as a deburring tool, a new method is investigated in this paper and validated by experimentation. The novelty of this work lies in the use of a simple deburring tool, which is usually used for manual deburring. In addition, the manipulator used is a collaborative robot that operates in hybrid motion force control. The characteristics of such robots gives human the ability to interact with the robot in a safe environment with little effort and safety measures, which was previously not possible for machining manufacturing purposes. Since the force and velocity of the robots play a decisive role in deburring, we have examined the lower and upper limits of these parameters through various experiments. To our knowledge, this is the first scientific work using a cobot and a simple hand deburring tool.
Optimised EMG pipeline for gesture classification

In the expanding field of robotic prosthetics, surface electromyography (sEMG) signals can be decoded to seamlessly control a robotic prosthesis to perform the desired gesture. It is essential to create a pipeline, which can acquire, process, and accurately classify sEMG signals in order to replicate the desired hand gesture in near real-time and in a reliable manner. In this study, an optimised pipeline is proposed. This pipeline encompasses the main stages of sEMG signal processing and hand gesture classification and implements a sliding window approach, which is the main focus of the optimisation. In this study, a range of different parameters and modelling approaches are evaluated. The main contributions of this work are a robust and extensive analysis of sliding window parameter selection and an optimised pipeline that could be implemented in practice with minimal overheads. The optimum pipeline is efficient and achieves accurate prediction of hand gestures with an uninterrupted processing pipeline.
Attenuation Correction for Myocardial Perfusion SPECT Imaging in the Image Domain

Attenuation correction (AC), as an essential component of quantitative SPECT imaging, is highly challenging in the absence of CT or transmission scanning in dedicated cardiac SPECT devices. This study investigated the potential of direct attenuation correction (AC) of 99mTc-sestamibi SPECT myocardial perfusion imaging (MPI) in the image domain using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT 99mTc-ses-tamibi images of 99 subjects were retrospectively used, considering non-attenuation corrected SPECT images as input and CT-based attenuation corrected (CT-AC) SPECT images as reference. Chang’s AC technique was also performed as a baseline for evaluating the performance of deep learning-based models. The quantitative accuracy of the attenuation corrected SPECT images was validated using the proposed methods considering the corresponding CT-AC SPECT images of 19 external datasets as reference. Image-derived metrics, including the voxel-wise mean absolute error (MAE), relative error (RE), structural similarity index measure (SSIM), as well as clinically relevant indices, such as total perfusion deficit (TPD), were calculated. The quantitative results obtained from the external validation dataset of the deep learning models exhibited good agreement with the reference CT-based attenuation corrected MPI-SPECT images. The deep learning models showed superior performance over Chang’s AC method. The ResNet and UNet models resulted in MAE of 20.24±17.63 and 13.65±11.23 and SSIM of 0.99±0.04 and 0.98±0.05, respectively. In comparison, Chang’s approach led to MAE and SSIM of 80.27±47.56 and 0.93±0.09, respectively. Similarly, the clinical evaluation showed a mean TPD of 12.78±9.22% and 12.57±8.93% for ResNet and UNet models, respectively, compared to TPD of 12.84±8.63% for reference SPECT CT-AC images. In contrast, Chang’s approach led to a mean TPD of 16.68±11.24%. The evaluation of deep learning approaches implemented in this work showed the promising potential of deep learning-based techniques in producing attenuation corrected MPI-SPECT images in the image domain without anatomical imaging.
Distant supervision for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma

Finding effective ways to perform cancer sub-typing is currently a trending research topic for therapy opti-mization and personalized medicine. Stemming from genomic field, several algorithms have been proposed. In the context of texture analysis, limited efforts have been attempted, yet imaging information is known to entail useful knowledge for clinical practice. We propose a distant supervision model for imaging-based cancer sub-typing in Intrahepatic Cholangiocar-cinoma patients. A clinically informed stratification of patients is built and homogeneous groups of patients are characterized in terms of survival probabilities, qualitative cancer variables and radiomic feature description. Moreover, the contributions of the information derived from the ICC area and from the peri tumoral area are evaluated. The findings suggest the reliability of the proposed model in the context of cancer research and testify the importance of accounting for data coming from both the tumour and the tumour-tissue interface. Clinical relevance - In order to accurately predict cancer prognosis for patients affected by ICC, radiomic variables of both core cancer and surrounding area should be exploited and employed in a model able to manage complex information.
A new multimodal device for atrial fibrillation detection: ECG quality analysis in healthy volunteers

In the context of increase in cardiovascular diseases in the aging population, including a high prevalence of atrial fibrillation (AF), the development of medical devices to ensure patient follow-up is of major interest. The purpose of this study is to assess the ECG signal quality of a one-lead in a new miniaturized device on healthy volunteers submitted to several conditions reflecting daily life activity. Our results show that the P wave identification is not enough reliable to consider the detection of its potential disappearance in case of AF. However, we show that the ECG signals can be used to robustly detect the RR intervals. To conclude, for rhythm disturbance detection, an automatic and specific analysis of RR variability has now to be integrated in this new multimodal device. Clinical relevance - This study confirms the potential interest of implantable medical device in the management of cardiac arrhythmias notably in the context of the individual follow up of aging patients
Prediction of Human Induced Pluripotent Stem Cell Formation Based on Deep Learning Analyses Using Time-lapse Brightfield Microscopy Images

We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to segment cells and a CNN is used to classify the segmented cells into eight types of cells during the reprogramming and hiPSC formation based on cellular morphology on the microscopy images. The numbers of respective types of cells in cell clusters before the hiPSC formation stage are used to predict if hiPSC regions can be well formed lately. Experimental results show good prediction by the criteria using the numbers of different cells in the clusters. Time-series images with respective types of classified cells can be used to visualize and quantitatively analyze the growth and transition among dispersed cells not in cell clusters, various types of cells in the clusters before the hiPSC formation stage and hiPSC cells.
The Capitalization of Area Payment into Land Rental Prices: Micro-evidence from Italy

This paper analyses the extent to which agricultural subsidies are capitalised into land rental price. By using Italian data at the farm level the analysis proposed in this paper innovates with respect to existing studies in different ways. Thanks to the long time span available in the FADN database it is possible to compare the two time periods, before and after the 2005 CAP reform, to test whether any change occurred as a result of the introduction of the decoupled payments scheme. In contrast to previous empirical literature, which has either focused on the unobserved farm-level heterogeneity issue or on the selectivity issue, the method proposed in this paper accounts for both simultaneously. Finally, the same method is extended to account for endogeneity of some covariates. Overall, the results in the paper confirm previous evidence, rejecting the hypothesis that agricultural payments are capitalized into land prices in both periods.
Efficacy of astragalus in the treatment of radiation-induced lung injury based on traditional Chinese medicine: A systematic review and meta-analysis of 25 RCTs

Background: Astragalus (Hedysarum Multijugum Maxim., Huangqi) is a Chinese herbal medicine, and according to the theory of traditional Chinese medicine (TCM), Chinese medicinal preparations containing astragalus can be used clinically to treat radiation-induced lung injury (RILI). To systematically review the efficacy and safety of Chinese medicinal preparations containing astragalus in the prevention and treatment of RILI by means of meta-analysis. Methods: A systematic literature on randomized controlled trials (RCTs) of prescriptions containing astragalus in the treatment of RILI by Pubmed, Embase, Web of Science, Cochrane Library, China Biomedical Literature Database, China National Knowledge Infrastructure, China Science and Technology Journal Database, WANFANG Database. The retrieval time is from the establishment of the database to January 18, 2022. Meta-analysis, heterogeneity test and sensitivity analysis were performed on eligible RCTs using Revman 5.4 software and STATA 17.0 software, and a “funnel plot” was used to analyze potential publication bias. Results: A total of 25 RCTs were included, including 1762 patients, and the most widely used drugs were heat-clearing and detoxifying, yin-nourishing and qi-nourishing. The prescriptions containing astragalus can significantly reduce the total incidence of RILI (P < .01), improve the total effective rate and cure rate of RILI (P < .01), improve the quality of life of patients, alleviate breathing difficulties and reduce the expression of inflammatory factors (P < .01), and no adverse reactions related to TCM treatment were reported. Conclusion: The traditional Chinese medicinal preparation containing astragalus can effectively alleviate the clinical symptoms of RILI, reduce the toxic side effects, and is safe to use in clinic.
Does noninvasive cerebellar stimulation improve the balance and walking function of patients with stroke: A meta-analysis of randomized controlled trials

Objectives: Investigating the efficacy and safety of noninvasive cerebellar stimulation in improving the balance and walking function of patients with stroke. Methods: We searched 7 databases for randomized controlled trials (RCTs) related to noninvasive cerebellar stimulation in the treatment of stroke. The Berg Balance Scale (BBS), 6-minute walk test (6MWT), and Barthel Index (BI) were used as the outcome indexes to evaluate balance, walking and activities of daily living (ADL). The quality of the research was evaluated using the Cochrane Risk of Bias Tool. A meta-analysis was performed to evaluate the difference between the noninvasive cerebellar stimulation and control groups. Heterogeneity tests were performed to assess differences in treatment effects across noninvasive cerebellar stimulation modalities. A sensitivity analysis was performed to evaluate the robustness of the results. Results: Seven studies were included, and 5 articles (71.43%) were rated as having a low risk of bias. Among the primary outcome indicators, 4 of the 7 articles were combined into the fixed effect model (I2 = 38%, P = .18). Compared with the control group, noninvasive cerebellar stimulation improved the BBS score, and the difference was statistically significant (mean difference [MD]: 3.00, 95% confidence interval [CI]: 1.10–5.40, P = .03); the sensitivity analysis showed that the statistical model was still stable after sequentially eliminating each article. Compared with the control group, noninvasive cerebellar stimulation improved the 6MWT results of patients with stroke (MD: 25.29, 95% CI: 4.86–45.73, P = .02). However, noninvasive cerebellar stimulation did not improve the BI (MD: 15.61, 95% CI: −7.91 to 39.13, P = .19). No safety problems or adverse reactions to noninvasive cerebellar stimulation were observed. Conclusions: Noninvasive cerebellar stimulation improves balance and walking function of patients with stroke, but its effect on ADL is uncertain. Due to the methodological weaknesses in the included trials, more RCTs are needed to confirm our conclusions.
Biomechanical characteristics of 2 different posterior fixation methods of bilateral pedicle screws: A finite element analysis

Background: To explore the biomechanical characteristics of 2 posterior bilateral pedicle screw fixation methods using finite element analysis. Methods: A normal L3-5 finite element model was established. Based on the verification of its effectiveness, 2 different posterior internal fixation methods were simulated: bilateral pedicle screws (model A) were placed in the L3 and L5 vertebral bodies, and bilateral pedicle screws (model B) were placed in the L3, L4, and L5 vertebral bodies. The stability and stress differences of intervertebral discs, endplates, screws, and rods between models were compared. Results: Compared with the normal model, the maximum stress of the range of motion, intervertebral disc, and endplate of the 2 models decreased significantly. Under the 6 working conditions, the 2 internal fixation methods have similar effects on the stress of the endplate and intervertebral disc, but the maximum stress of the screws and rods of model B is smaller than that of model A. Conclusions: Based on these results, it was found that bilateral pedicle screw fixation in 2 vertebrae L3 and L5 can achieve similar stability as bilateral pedicle screw fixation in 3 vertebrae L3, L4, and L5. However, the maximum stress of the screw and rod in model B is less than that in model A, so this internal fixation method can effectively reduce the risk of fracture. The 3-dimensional finite element model established in this study is in line with the biomechanical characteristics of the spine and can be used for further studies on spinal column biomechanics. This information can serve as a reference for clinicians for surgical selection.
Holistic shape-based object recognition using bottom-up image structures

Object recognition performance that rivals human ability is one of the primary goals of computer vision research. While recognition may take many forms, key tasks include detection, estimating object pose, and segmenting the object from the background. This thesis explores the use of holistic shape matching for recognition using bottom-up image structures such as image segments and contours for all of these tasks. Holistic shape matching utilizes global information about object shape for matching, rather than local image features which often contain too little information to match reliably to the object model. By examining different tasks related to object recognition, we demonstrate the value of holistic shape matching in a broad range of problems, including perceptual grouping, human pose estimation, and object recognition. First, we introduce a method for perceptual grouping of contours in an image into larger groups that uses holistic shape matching to estimate the motion of image contours to a second, related image and group them according to similarties in motion. Holistic shape matching provides scoring for different motion hypotheses, and the final grouping is achieved using a min-cut graph cut to infer the cluster assignment for each contour. Secondly, we describe a method for human pose estimation using image segments that incrementally merges segments into hypotheses for increasingly larger regions of the human body. These hypotheses are verified by matching against a set of shape exemplars using a shape matching method that is articulation-invariant and incorporates holistic shape information. Lastly, we present a two-step method for automatically learning an object detector for an object category from positive and negative images annotated with bounding boxes. In the first step, the object shape is learned from bottom-up image contours extracted in the positive images by searching for "lucky" contours that can explain large portions of the shape of positive examples. Given the learned shape, the second step trains a discriminative object detector that matches the shape against contours, emphasizing shape features that provide good detection performance. We compare against baselines and previous work that do not use holistic evaluation of shape features to demonstrate its value.
The effect of domain knowledge on discourse relation inferences: Relation marking and interpretation strategies

It is generally assumed that readers draw on their background knowledge to make inferences about information that is left implicit in the text. However, readers may differ in how much background knowledge they have, which may impact their text understanding. The present study investigates the role of domain knowledge in discourse relation interpretation, in order to examine how readers with high vs. low domain knowledge differ in their discourse relation inferences. We compare interpretations of experts from the ﬁeld of economics and biomedical sciences in scientiﬁc biomedical texts as well as more easily accessible economic texts. The results show that high-knowledge readers from the biomedical domain are better at inferring the correct relation interpretation in biomedical texts compared to low-knowledge readers, but such an effect was not found for the economic domain. The results also suggest that, in the absence of domain knowledge, readers exploit linguistic signals other than connectives to infer the discourse relation, but domain knowledge is sometimes required to exploit these cues. The study provides insight into the impact of domain knowledge on discourse relation inferencing and how readers interpret discourse relations when they lack the required domain knowledge.
Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric Grids

We propose a simple yet effective method for clustering finite elements to improve preprocessing times and rendering performance of unstructured volumetric grids without requiring auxiliary connectivity data. Rather than building bounding volume hierarchies (BVHs) over individual elements, we sort elements along with a Hilbert curve and aggregate neighboring elements together, improving BVH memory consumption by over an order of magnitude. Then to further reduce memory consumption, we cluster the mesh on the fly into sub-meshes with smaller indices using a series of efficient parallel mesh re-indexing operations. These clusters are then passed to a highly optimized ray tracing API for point containment queries and ray-cluster intersection testing. Each cluster is assigned a maximum extinction value for adaptive sampling, which we rasterize into non-overlapping view-aligned bins allocated along the ray. These maximum extinction bins are then used to guide the placement of samples along the ray during visualization, reducing the number of samples required by multiple orders of magnitude (depending on the dataset), thereby improving overall visualization interactivity. Using our approach, we improve rendering performance over a competitive baseline on the NASA Mars Lander dataset from 6× (1 frame per second (fps) and 1.0 M rays per second (rps) up to now 6 fps and 12.4 M rps, now including volumetric shadows) while simultaneously reducing memory consumption by 3×(33 GB down to 11 GB) and avoiding any offline preprocessing steps, enabling high-quality interactive visualization on consumer graphics cards. Then by utilizing the full 48 GB of an RTX 8000, we improve the performance of Lander by 17 × (1 fps up to 17 fps, 1.0 M rps up to 35.6 M rps).
Parallelized Two-Stage Object Detection in Cluttered RGB-D Scenes by

Modern graphics hardware (GPUs) are an amazing computational resource, but only for algorithms with suitable structure. Computer vision algorithms have many characteristics in common with computer graphics algorithms, in particular, they repeat some operations, such as feature computations, at many places in the image. However, there are also more global operations, such as finding nearest neighbors in feature space, that present more of a challenge. In this thesis, we showed how a state-of-theart object detector, based on RGB-D images, could be parallelized for use on GPUs. By using nVidia's CUDA platform we improved the running times of critical sections up to 38 times. We also built a two-stage pipeline that improves multiple object detection in cluttered scenes. The first stage aims to achieve high precision, even at the cost of lower recall, by detecting only the less occluded objects. This results in large fraction of the scene being labeled which enables the algorithm in the second stage to focus on the less visible objects that would otherwise be missed. We analyze the performance of our algorithm and lay grounds for the future work and extensions. Thesis Supervisor: Leslie Pack Kaelbling Title: Panasonic Professor of Computer Science and Engineering Thesis Supervisor: Tomis Lozano-P6rez Title: Professor of Computer Science and Engineering