Brain MRI Tumor Segmentation with Adversarial Networks

Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks. In particular, we extend SegAN, successfully applied to the same task in a previous work, in two respects: (i) we used a different model input and (ii) we employed a modified loss function to train the model. We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS). First, we trained and tested some segmentation models assuming the availability of all the major MRI contrast modalities, i.e., T1-weighted, T1 weighted contrast-enhanced, T2-weighted, and T2-FLAIR. However, as these four modalities are not always all available for each patient, we also trained and tested four segmentation models that take as input MRIs acquired only with a single contrast modality. Finally, we proposed to apply transfer learning across different contrast modalities to improve the performance of these single-modality models. Our results are promising and show that not SegAN-CAT is able to outperform SegAN when all the four modalities are available, but also that transfer learning can actually lead to better performances when only a single modality is available

Distance and Similarity Measurements of P Waves Before and After Pulmonary Vein Isolation in Patients with Atrial Fibrillation

[EN] This study aimed to assess electric markers obtained from the surface electrocardiogram in order to analyse significant differences before and after pulmonary vein isolation in patients who suffered from paroxysmal atrial fibrillation. 30 patients who underwent catheter ablation (in order to permanently restore sinus rhythm and stop atrial fibrillation episodes) were included in the study. Both surface electrocardiogram and intracavitary recordings were simultaneously acquired starting some minutes before catheter ablation began until the whole procedure successfully ended. P-waves have been delineated on V1 lead, and measurements of distances and similarities between them have been obtained to compare the recordings. It has been found that distances between Pwaves significatively decrease (about 14%) whereas similarities significatively increase (about 3%) after catheter ablation. The use of these features would help to identify the success of the catheter ablation procedure, which is the main objective of this preliminary study: the non-invasive identification of spontaneous reconnection of pulmonary veins, the main cause of the arrhythmia recurrences.N. Ortigosa acknowledges the support from Generalitat Valenciana under grant Prometeo/2017/102; from Spanish Ministerio de Educacion, Cultura y Deporte en el marco del Programa Estatal de Promocion del Talento y su Empleabilidad en I+D+i -Subprograma Estatal de Movilidad- under grant Jose Castillejo CAS18/00396; from Spanish MINECO under grant MTM2016-76647-P, and from VLC-BIOMED 2017 (Universitat de Valencia and Hospital La Fe / IIS La Fe) under grant 10-ARVEAP-GALBIS-CANO2017-A.Ortigosa, N.; Cano, Ó.; Mainardi, L. (2019). Distance and Similarity Measurements of P Waves Before and After Pulmonary Vein Isolation in Patients with Atrial Fibrillation. IEEE. 87-90. https://doi.org/10.1109/EMBC.2019.8857563S879

Online Detection of P300 and Error Potentials in a BCI Speller

Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances

Time and foreign exchange markets

The definition of time is still an open question when one deals with high frequency time series. If time is simply the calendar time, prices can be modeled as continuous random processes and values resulting from transactions or given quotes are discrete samples of this underlying dynamics. On the contrary, if one takes the business time point of view, price dynamics is a discrete random process, and time is simply the ordering according which prices are quoted in the market. In this paper we suggest that the business time approach is perhaps a better way of modeling price dynamics than calendar time. This conclusion comes out from testing probability densities and conditional variances predicted by the two models against the experimental ones. The data set we use contains the DEM/USD exchange quotes provided to us by Olsen & Associates during a period of one year from January to December 1998. In this period 1,620,843 quotes entries in the EFX system were recorded

Time-Varying Spectral Analysis of a Single EEG Channel: Application in an Affective Protocol

Neural correlates of emotions have been widely investigated using noninvasive sensor modalities. These approaches are often characterized by a low level of usability and are not practical for real-life situations. The aim of this study is to show that a single EEG electrode placed in the central region of the scalp is able to discriminate emotional characterized events with respect to a baseline period. Emotional changes were induced using an imagery approach based on the recall of autobiographical events characterized by four basic emotions: "Happiness", "Fear", "Anger" and "Sadness". Data from 17 normal subjects were recorded on Cz position according to the International 10-20 System. After preprocessing and artifact detection phases, raw signals were analyzed through a time-variant adaptive autoregressive model to extract EEG characteristic spectral components. We considered 5 frequency bands, i.e. the classical EEG rhythms, namely the delta band (δ), [1-4] Hz, the theta band (θ), [4-6] Hz, the alpha band (α), [6-12] Hz, the beta band (β), [12-30] Hz, and the gamma band (γ), [30-50] Hz. The relative powers of the EEG rhythms were used as features to compare the experimental conditions. Our results show statistically significant differences when comparing the power content in the gamma band of baseline events versus emotionally characterized events. Particularly, we found a significant increase in gamma band relative power in 3 out of 4 emotionally characterized events, i.e. “Happiness” “Sadness” and “Anger". In agreement with previous studies, our findings confirm the presence of a possible correlation between broader high frequency cortical activation and affective processing of the brain. The present study shows that the use of a single EEG electrode represents a possible advantageous premise for the assessment of the emotional state with a minimally invasive set-up

Quantification of Spatial Repolarization Heterogeneity: Testing the Robustness of a New Technique

Abstract The V-index is a recently-proposed metric related to repolarization heterogeneity (RH) Introduction Spatial heterogeneity of ventricular repolarization is a key quantity for the development of arrhythmias. Despite many methods have been proposed and investigated in the past [1-3], a non-invasive quantification of Repolarization Heterogeneity (RH) is still an open issue We recently proposed an estimator of the standard deviation of RH, which was named "V-index" Although the performances of the method have been deeply investigated in the original paper Method An estimate of repolarization heterogeneity Let us suppose to subdivide the myocardium in "nodes", each node m sharing a common transmembrane potential (TMP), D(t), but having a specific repolarization time given by ρ m =ρ + Δρ m . At each node m, the repolarization delay Δρ m is the deviation from the average repolarization timeρ = 1 M M m=1 ρ m in the given heartbeat. We have recently [5] introduced a simple model to describe the distribution of these delays Δρ m (k) among beats, being k the beat index. In particular we set: where ϑ m models the spatial variability of the repolarization times for a given subject at a given HR, and ϕ m (k) describes difference in repolarization times which are observable among successive beats

A multivariate time-frequency method to characterize the influence of respiration over heart period and arterial pressure

Respiratory activity introduces oscillations both in arterial pressure and heart period, through mechanical and autonomic mechanisms. Respiration, arterial pressure, and heart period are, generally, non-stationary processes and the interactions between them are dynamic. In this study we present a methodology to robustly estimate the time course of cross spectral indices to characterize dynamic interactions between respiratory oscillations of heart period and blood pressure, as well as their interactions with respiratory activity. Time-frequency distributions belonging to Cohen's class are used to estimate time-frequency (TF) representations of coherence, partial coherence and phase difference. The characterization is based on the estimation of the time course of cross spectral indices estimated in specific TF regions around the respiratory frequency. We used this methodology to describe the interactions between respiration, heart period variability (HPV) and systolic arterial pressure variability (SAPV) during tilt table test with both spontaneous and controlled respiratory patterns. The effect of selective autonomic blockade was also studied. Results suggest the presence of common underling mechanisms of regulation between cardiovascular signals, whose interactions are time-varying. SAPV changes followed respiratory flow both in supine and standing positions and even after selective autonomic blockade. During head-up tilt, phase differences between respiration and SAPV increased. Phase differences between respiration and HPV were comparable to those between respiration and SAPV during supine position, and significantly increased during standing. As a result, respiratory oscillations in SAPV preceded respiratory oscillations in HPV during standing. Partial coherence was the most sensitive index to orthostatic stress. Phase difference estimates were consistent among spontaneous and controlled breathing patterns, whereas coherence was higher in spontaneous breathing. Parasympathetic blockade did not affect interactions between respiration and SAPV, reduced the coherence between SAPV and HPV and between respiration and HPV. Our results support the hypothesis that non-autonomic, possibly mechanically mediated, mechanisms also contributes to the respiratory oscillations in HPV. A small contribution of sympathetic activity on HPV-SAPV interactions around the respiratory frequency was also observed

Mixed Reality and Artificial Intelligence: a Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (&#x201C;Patient selection&#x201D; and &#x201C;Scrolling through radiograph&#x201D;) with respect to the second attempt, but without statistically significant difference (respectively $p$ &#x003D; 0.14 and $p$ &#x003D; 0.13, p < 0.05 ). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9&#x0025;) strongly agreed that it can be used effectively for preoperative planning. Six (54.5&#x0025;) agreed and two of them (18.2&#x0025;) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage