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No, technology isn't making us lonely—it's bringing us closer to each other. others, especially introverts, may prefer to beat loneliness by interacting online.

Share Flipboard Email. Helmenstine holds a Ph. She has taught science courses at the high school, college, and graduate levels. Updated August 14, Stewart's liquid laundry bluing 1 tablespoon or 15 ml household ammonia Food coloring optional. Continue Reading.

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Support Find support for a specific problem on the support section of our website. Get Support. Feedback Please let us know what you think of our products and services. Give Feedback. Get Information. Laboratory of Polymer Chemistry and Technology, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Macedonia, Greece Interests: synthesis and characterization of polyesters; development of biobased polymers; biodegradable polymers; polymer composites and nanocomposites; synthesis and characterization of copolymers; polymer blends; recycling of polymers with various techniques; modification of natural polymers; polymer for wastewater treatment pollutant removal; polymers for tissue engineering and drug delivery applications; drug—polymer solid dispersions; drug targeting; drug nanoencapsulation and microencapsulation Special Issues and Collections in MDPI journals Special Issue in Polymers : Biodegradable and Biobased Polyesters Special Issue in Molecules : Polymer Composites and Nanocomposites with Enhanched Properties Special Issue in Polymers : Biobased and Biodegradable Polymers.

Bernhard V. Seth B. Darling E-Mail Website. We identified several cardiac fiducial points on the GCG x- and y-axis signals and estimated the cardiac time intervals from ECG Q to the delineated points. We assessed the agreement between the estimated and reference time intervals using the Bland-Altman technique.

In comparison to GCG x-axis, the GCG y-axis time intervals had smaller bias and variability with reference to the TDI measurements, which shows that the GCG y-axis estimates provide better approximations for cardiac intervals. However, the finding of this study does not confirm that CGC is an accurate tool for measuring the cardiac timing intervals.

Keywords: Cardiovascular, respiratory, and sleep devices - Sensors Abstract: This minisymposium contribution focuses on the ongoing studies where we have provided ballistocardiogram BCG and electrocardiogram ECG sensing weighing scales to patients with heart failure HF for serial measurements at home. The BCG is a measurement of the mechanical aspects of cardiovascular function. The ultimate goal of the study is to assess whether BCG and ECG signals can provide value in predicting exacerbations for patients with HF at home, and thus be used to titrate care remotely.

Thus far, we have recruited a total of 16 patients with HF for a total of patient-days of recordings. Of these recordings, were found to be usable based on signal quality indices i. Moreover, in one subject, the variability of BCG signal parameters over time was high at the beginning of the day recording period, then stabilized at the end — this subject had inadvertently placed himself on a beta-blocker for the period of variability. We will present the findings thus far from our study, and discuss key lessons we have learned.

Keywords: Neural interfaces - Bioelectric sensors , Neural interfaces - Tissue-electrode interface , Neural interfaces - Body interfaces Abstract: Ear-EEG is a recording method where EEG signals are acquired from electrodes placed on an earpiece inserted into the ear. Previously reported ear-EEG recordings have been performed with wet electrodes, and the objective of this study was to develop and evaluate dry-contact electrode ear-EEG.

A novel dry-contact ear-EEG platform, comprising electrodes embedded in a soft-earpiece, was developed. The platform was evaluated in a study of four EEG paradigms: auditory steady-state response, steady-state visual evoked potential, mismatch negativity, and alpha band modulation. With both the measuring electrode and the reference electrode located within the ear, statistically significant p Keywords: Neuromuscular systems - Locomotion , Neural interfaces - Bioelectric sensors , Neural signal processing Abstract: We used high-density electroencephalography EEG to evaluate electrocortical dynamics during obstacle navigation using a novel dual-layer electrode approach for noise cancellation.

After validating the technique on a phantom head, we collected data from subjects walking and running on a treadmill as they stepped over obstacles. Data revealed event related synchronizations in premotor, primary motor, and posterior parietal cortices tied to obstacles appearing on the treadmill. The results show it is possible to document the timing of brain network activity synchronized to reactive adjustments in human locomotion using mobile EEG.

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Keywords: Neural signal processing Abstract: This paper looks at the existing state-of-the-art use of wearable EEG in the area of sleep monitoring and the future challenges for the development of EEG-based wearable systems for the diagnosis of neurological sleep disorders. Keywords: Neural interfaces - Body interfaces , Neural signal processing , Neural stimulation Abstract: Closed loop EEG systems monitor the EEG and apply real-time signal processing to trigger state dependent auditory, visual or electrical stimulation. This talk will overview the technical platforms we are creating for delivering such interventions for personalized brain therapies.

In order to overcome these practical challenges we have developed and tested soft, pliable electrodes and novel electrode form factors from existing materials. Keywords: Brain physiology and modeling , Human performance , Neural interfaces - Body interfaces Abstract: Novel methods of EEG data acquisition, cleaning and interpretation are notoriously difficult to validate due to a lack of ground truth.

Keywords: Diagnostic devices - Physiological monitoring , Cardiovascular assessment and diagnostic technologies Abstract: We describe the design of a low-power, wireless sensor for biomedical applications, taking a Bluetooth-enabled oximeter and heart rate monitor as example. The two-chip solution consists of a battery-operated, radio-enabled SoC for signal processing and communication, and an optoelectronic front end for sensing.

The whole system fits in an ear lobe clip. Keywords: Clinical laboratory, assay and pathology technologies , Diagnostic devices - Physiological monitoring Abstract: A biosensor system has been developed and tested which uses microfabricated gold electrodes which are chemically modified. Gold nanoparticles were chemically bound to the electrodes and a resulting electrical impedance change was measured.

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This type of test can be modified with biological components to create a versatile biosensor system for medical diagnostics, environmental monitoring, and research applications. However the computing resources needed to accurately predict nucleotide sequences from the raw measurements "at speed" are substantial and preclude portability.

As a step towards addressing this issue, we present a recurrent neural network hardware design intended for embedded nucleic acid basecalling within the sequencing pipeline. Keywords: Diagnostic devices - Physiological monitoring , Wearable or portable devices for vital signal monitoring Abstract: Printed electronics has become of great interest over the last few years due to its advantages of a reduction of fabrication cost and the possibility to print on flexible substrates.

Here we present the fabrication of temperature and humidity sensors by using thermal transfer printed interdigitated electrodes covered by graphene oxide. Graphene oxide acts as the temperature and humidity sensing material by changing its electrical properties as a function of the ambient conditions.

These type of printed sensors could easily be integrated into biomedical applications such as smart bandages.


Keywords: Diagnostic devices - Physiological monitoring , Clinical laboratory, assay and pathology technologies , Neuromodulation devices Abstract: We present preliminary work on the detection of neurotransmitters NTs in a microfluidic channel by visible light spectroscopic means. NTs are not active in the visible spectrum in general, and that complicates their detection by direct spectroscopic methods.

Nevertheless, some NTs interfere with the absorption spectrum of gold nanoparticles AuNPs by shifting their nm maximum absorbance wavelength. This opens the door to NT detection with visible light optical means, in addition to the possibility to integrate the whole system on a single chip for continuous measurement in real time. Keywords: Medical devices interfacing with the brain or nerves , Wireless technologies for interrogation of implantable therapeutic devices , Diagnostic devices - Physiological monitoring Abstract: This paper presents the recent advances in CMOS capacitive sensors that operate based on a charge based capacitance measurement CBCM technique.

In this paper, the author briefly discusses the challenges in the design and implementation of core-CBCM capacitive sensors. Furthermore, the advantages of these high throughput sensors are put forward by demonstrating experimental results for monitoring the cellular activities,. Recent database analyses and efforts in patient phenotyping have shown that personalized treatment is possible with the potential for improved compliance to chronic therapy.

In this paper, we report recent observations of elevated plant gain in obese adolescents and preterm-born children. We propose that elevated plant gain may be a manifestation of abnormal lung gas exchange in these subpopulations using a mathematical model. It has been argued that this expansion of current practice could yield performance improvements as well as allowing differentiation of patient cohorts on a more systematic basis. Keywords: Optical imaging - Coherence tomography , Ophthalmic imaging and analysis Abstract: Optical coherence tomography allows in vivo, non-invasive imaging of tissue.

We present two systems developed for the analysis of OCT images. ASHIMA is developed for dermatological imaging to identify and separate the components of a skin OCT image for enhanced visualization and assessment of skin layers. Such systems could assist clinicians in the analysis of OCT images, with potential applications in screening and workflow improvement. Many papers for blood vessels extraction and abnormalities detection have been published. In the recent years, deep convolution neural network DCNN has been applied to such studies instead of image features base techniques.

This paper describes about retinal blood vessels extraction and microaneurysms detection by using patch based CNN. We have been studying an automated scheme for detection of retinal nerve fiber layer defect NFLD , which is one of the earliest signs of glaucoma on retinal fundus images. In our previous study, we investigated the use of a convolutional neural network CNN with deconvolutional layers for segmentation of NFLD regions.

We also investigated the use of two networks in a cascade form to improve detection performance. Keywords: Image feature extraction , CT imaging applications Abstract: To identify prognostic imaging biomarkers in hepatocellular carcinoma HCC with biological interpretations by associating imaging features and gene modules. For the patients with CECT imaging data and gene expression profiles, intra-tumor partition was performed resulting in three spatially distinct subregions.

Quantitative imaging features were extracted from each subregion. Prognostic gene modules were obtained, and their biological functions were annotated. The imaging features that significantly correlated with prognostic gene modules were selected, and their prognostic capabilities for overall survival OS were evaluated.

The volume fraction of subregion, which was significantly correlated with all prognostic gene modules representing cancer-related interpretation, was predictive of OS. The texture feature cluster prominence in subregion, which was correlated with the prognostic gene module representing lipid metabolism and complement activation, also had the ability to predict OS. Imaging features of subregions have potentials to be predictors of OS with interpretable biological meaning.

We aim at the characteristics of 3D low dose CT image volume data, and design an automatic segmentation algorithm for three-dimensional lung parenchyma to determine the range of lung cancer screening at high speed. Moreover, on the basis of the multi task deep neural network architecture, the effective three-dimensional convolution operator is designed and the 3D deep neural network of multi task learning is constructed for this task.

Keywords: Health technology - Verification and validation , Clinical engineering , Neuromodulation devices Abstract: This paper describes an electric field probe that could be used both in air and in a tissue to measure quasi-static electric fields of biomedical instruments from Hz to kHz. The probe uses a small dipole antenna connected via a long shaft with a simplified Dyson balun to an optical isolation amplifier with low common-mode gain. Induced electric field measured in air are presented for a quasi-static solenoidal field. Keywords: Muscle stimulation , Neural stimulation including deep brain stimulation , Computer modeling for treatment planning Abstract: One major contributor to the degradation of quality of life for a significant portion of the adult population is chronic pain.

In many cases, pharmacological solutions are limited in efficacy and alternative treatments, such as Transcutaneous Electrical Nerve Stimulation TENS , are pursued. This paper describes simulation results that estimate the electric fields generated within an accurate computational human phantom placed within a full-body birdcage resonator operating at kHz. The device may operate as a novel and effective TENS device, capable of deep stimulation over large body areas. Keywords: Computer model-based assessments for regulatory submissions , Wearable or portable devices for vital signal monitoring Abstract: Radio frequency wave propagation near the surface of a human body is highly sensitive to a number of items, including skin geometry, material composition and proximity to internal air-filled cavities.

This study develops an anatomically realistic model of a human ear canal integrated into a full body computational phantom to address this final factor and examines, through numerical simulation, the impact of this level of detail on the power transmission of two-port and larger networks operating near or on the human body in the UHF band. Keywords: Wireless technologies for interrogation of implantable therapeutic devices Abstract: Comprehensive study of radio waves propagation for ingestible electronics is a very challenging task. Obtaining physical measurements is nearly impossible.

And, although limited experimentation on animals or liquid phantom measurements are possible; the results are not quite reflective of the complex and inhomogeneous human body environment. To initiate the study on UWB propagation from a wireless capsule endoscopy, a flexible and interactive immersive platform containing and an enhanced 3D body model has been developed. This platform enables researchers to conduct a comprehensive study of the UWB propagation channel for WCE applications.

Keywords: Computer model-based assessments for regulatory submissions , Image-guided devices - MRI-compatible instrumentation and device management , Cochlear implant Abstract: Implant safety studies typically require a large number of simulations to test the compliance in various positions of the human body model HBM. Applying the Huygens Box concept allows a significant speed-up of the process. The approach allows to first design the MRI system based on the full simulation domain and then replacing the MRI coil with the equivalent fields on a surface enclosing the patient for following calculations with variations in terms of implant type and location or uncertain tissue properties.

A speed-up of a factor of 10 and more depending on the complexity of the full run can be reached for one implant evaluation at one position. Speed-up increases vastly with the number of evaluations. The presentation or final paper will also include investigations on changes in the HBM like variations of tissue properties or breathing.

Keywords: Image-guided devices - RF and microwave ablation , Image-guided devices - Interstitial thermal therapy , Computer modeling for treatment planning Abstract: Image-guided microwave ablation is clinically used to treat tumors in the liver and other organs. When treating large tumors, physicians may use multiple antennas simultaneous to rapidly create large volume ablation zones Pre-clinical experimental and simulation studies to characterize ablation patterns created by multiple antennas often presume parallel antenna insertion, which may not be possible in clinical practice.

We employed coupled electromagnetic — heat transfer simulations, incorporating temperature dependent tissue dielectric properties, to assess the impact of antenna misalignment on ablation zone profiles. Modeling results were validated with experiments in ex vivo liver tissue. For inter-antenna spacing in the range of 10 — 20 mm, the Dice Similarity Coefficient between parallel and non-parallel ablation zones ranged between 0. Transfer learning is a promising tool to solve this problem, which relaxes the hypothesis that training data must be independent and identically distributed with the test data.

We construct a sophisticated electroencephalography EEG signal representation and obtain an efficient EEG feature extractor through manifold constraints-based joint adversarial training with training data from other domains. EEG signal is more easily distinguished in the feature space mapped by the feature extractor. Negative transfer is one of the most challenging problems in transfer learning. In our approach, we apply manifold constraints to overcome this problem, which can avoid the geometric manifolds in the target domain being destroyed.

The experiments demonstrate that our approach has many advantages when applied to EEG classification tasks. This study also found that the frontal pole was the most useful region in the prefrontal cortex. However, most of the BCI systems, such as Pspeller, can only discriminate among options that have been given in advance. Therefore, the ability to decode the state of a person's perception and recognition, as well as that person's fundamental intention and emotions, from cortical activity is needed to develop a more general-use BCI system. In this study, two experiments were conducted.

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First, articulations were measured for Japanese monosyllabic utterances masked by several levels of noise. Second, auditory brain magnetic fields evoked by the monosyllable stimuli used in the first experiment were recorded, and neuronal current sources were localized in regions associated with speech perception and recognition — the auditory cortex BA41 , the Wernicke's area posterior part of BA22 , Broca's area BA22 , motor BA4 , and premotor BA6 areas.

Although the source intensity did not systematically change with SNR, the peak latency changed along SNR in the posterior superior temporal gyrus in the right hemisphere. The results suggest that the information associated with articulation is processed in this area. In addition, recent studies have shown that deep learning, in particular recurrent neural networks RNNs , provide promising approaches for decoding of large-scale neural data. These approaches involve computationally intensive algorithms with millions of parameters.

In this context, an important challenge in the application of neural decoding to next generation brain-computer interfaces for complex human tasks is the development of low-latency real-time implementations. We provide a proof of concept in the context of decoding dimensional spectrotemporal representation of spoken words from simulated 10, neural channels. In this particular case, the LSTM model included 4,, parameters.

In addition to providing multiple communication interfaces for the BCI system, the NeuroCoder platform can achieve sub-millisecond real-time latencies.

To mitigate risks of open brain surgery, we previously developed a stent-electrode array that can be delivered to the cortex via cerebral vessels. Following implantation of a stent-electrode array Stentrode in a large animal model, we investigated the longevity of high-quality signals, by measuring bandwidth in animals implanted for up to six months; no signal degradation was observed. We also investigated whether bandwidth was influenced by implant location with respect to the superior sagittal sinus and branching cortical veins; it was not.

Finally, we assessed whether electrode orientation had an impact on recording quality. There was no significant difference in bandwidths from electrodes facing different orientations. Consequently, a minimally invasive surgical approach combined with a stent-electrode array is a safe and efficacious technique to acquire neural signals over a chronic duration.

However, this technology is not widely adopted by people with late-stage amyotrophic lateral sclerosis ALS due to poor effectiveness. In this study, we attempt to assess the cognitive state of a completely locked-in ALS subject, and her ability to use motor imagery-based BCI for control. The subject achieves above chance level accuracies for both open loop We also observe a prominent theta oscillation with peak frequency at 4. Quantification shows that the theta oscillatory power increases during motor imagery tasks compared to idle tasks for both open-loop as well as closed-loop BCI tasks.

Furthermore, for closed-loop sessions, theta oscillation power correlates positively with feedback accuracy during movement tasks, and negatively with feedback accuracy during idle tasks. The traditional SSVEP extraction methods can effectively identify the target frequency contained in original EEG, however, the required data length usually lasts a few seconds. BSR is very sensitive to amplitude mutation and frequency fluctuation of the input signal, making the output difference can be used for the detection of the target frequency.

The processing results illustrate that the proposed method not only has a high recognition accuracy, but also effectively shortens the recognition time, thus improving the calculating speed. Keywords: Data mining and processing in biosignals Abstract: A mono-feature fuzzy index that evaluates the stress level from one feature extracted from ECG or GSR is presented.

It is build using several measures of the feature recorded when the subject is at rest. The mono-feature fuzzy index can be merged in a multi-feature stress index without any tuning. It can be used to select relevant features and to detect stress. The performance of the stress index is analyzed on a data set made of time periods of time when 20 subjects had to perform stressful tasks and corresponding control tasks. The stress was induced by 4 different tasks. Interesting conclusions could also be made on the tasks ability to induce stress.

Keywords: Data mining and processing in biosignals , Signal pattern classification , Physiological systems modeling - Signal processing in physiological systems Abstract: Hot flashes HF are intense, transient feelings of heat usually accompanied with flushed skin and sweating that are experienced by women around the time of menopause. HFs are associated with poor quality of life and increased cardiovascular risk. Automatic detection of HF occurrence and precise timing of HF onset could provide unique insight into the physiology of the HF and its effect on the cardiovascular system.

A novel automatic algorithm is proposed for the detection of HFs occurrence and timing from the sternal skin conductance signal that is robust to noise and artifacts. ECG-derived heart rate pattern variations are studied prior to the detected HF onset. The algorithm is validated against expert detected HFs over hours of sleep data collected from 12 perimenopausal women.

Application of this algorithm along with fusion of other simultaneously recorded physiological measures has the potential to advance understanding of the HF.

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Keywords: Data mining and processing - Pattern recognition , Neural networks and support vector machines in biosignal processing and classification , Signal pattern classification Abstract: Electromyographic activities EMG generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing them as a promising modality for prosthetic and cybernetic applications.

This paper presents a comparative analysis between different recurrent neural network RNN configurations for EMG-based hand gesture classification. Furthermore, the effects of an attention mechanism and varying learning rates are evaluated. Keywords: Data mining and processing in biosignals , Signal pattern classification Abstract: The aim of this study is to investigate the validity of an entropy-based objective assessment of cerebellar ataxia patients performing rhythmic tapping. Previous research conducted, particularly in time and frequency domains, tested the adherence of patients to more stringent experimental requirements.

These requirements may inadvertently cause higher level brain functions to influence the performance and possibly obscure the cerebella related disabilities in the data stream. In this study, a multiscale entropy-based learning process that overcomes this practical limitation was considered. In particular, assessment techniques with less restrictions on the tapping duration were considered. Thirty-three patients were engaged in the test, with three levels of severity 0 normal , 1 moderate and 2 severe ranked by specialist clinicians.

The performance of each model was evaluated using leave-one-out cross validation. Strong correlations with clinical assessment-based scoring were observed with the entropy based approach for both tests, although the correlation with time-frequency features were less convincing. Keywords: Data mining and processing in biosignals Abstract: In this paper an analysis of compression schemes based on compressed sensing CS and predictor techniques for neural signals is presented. The focus is on how much a compression algorithm can reduce data while not affecting the subsequent signal processing.

Since neural signals are processed by means of spike sorting algorithms the evaluation is not trivial and not well defined, since there exists in fact many different ways to detect and cluster the spikes. Evaluating how much a compression scheme affects the result of spike sorting programs is a crucial step before implementing such compression technique. In the analysis two use cases are evaluated: in the first, spikes are detected and extracted and only thereafter compressed.

In the second case, no information on the spikes is available and the whole raw signal is compressed. When dealing only with spike frames CS offers great compression at almost no loss, in the case of the whole recording its performances are greatly impaired and delta compression outperforms it in terms of data reduction and spike sorting results. Keywords: Image reconstruction - Performance evaluation , Image reconstruction - Fast algorithms , Iterative image reconstruction Abstract: A novel region-based method to track beating heart is proposed.

Sparse statistical pose modeling is used to reconstruct the region of interest ROI on beating heart surface. Firstly, a high-complexity thin plate spline is employed to pre-reconstructed the ROI of a series of frames. The 3D pose data of the ROI from the pre-reconstructed results are extracted to train a low-complexity model based on the sparse statistical analysis. The new trained low-complexity model is robust and efficient for ROI reconstruction of the following frames. The proposed model significantly reduces the redundant degrees of freedom to fit the surface of the heart.

A constraint item is added to the objective function which describes the 3D tracking problem to avoid erroneous convergence of the efficient second-order minimization ESM optimization algorithm. The proposed method is evaluated on the phantom heart video and the in vivo video obtained by the da Vinci surgical system. Keywords: Cardiac imaging and image analysis , Magnetic resonance imaging - Cardiac imaging , Magnetic resonance imaging - Dynamic contrast-enhanced MRI Abstract: The use of implantable cardiac devices has in- creased in the last 30 years.

Cardiac resynchronisation therapy CRT is a procedure which involves implanting a coin sized pacemaker for reversing heart failure. The pacemaker electrode leads are implanted into cardiac myocardial tissue. The optimal site for implantation is highly patient-specific. Most implanters use empirical placement of the lead. One region identified to have a poor response rate are myocardial tissue with transmural scar. Studies that precisely measure transmurality of scar tissue in the left ventricle LV are few. Most studies lack proper validation of their transmurality measurement technique.

This study presents an image analysis technique for computing scar transmurality from late-gadolinium enhancement MRI. The technique is validated using phantoms under a CRT image guidance system. The study concludes that scar transmurality can be accurately measured in certain situations and validation with phantoms is important.

Keywords: Image segmentation , Ultrasound imaging - Cardiac , Cardiac imaging and image analysis Abstract: Segmentation of the left ventricle LV in temporal 3D echocardiography sequences poses a challenge. However, it is an essential component in generating quantitative clinical measurements for the diagnosis and treatment of various cardiac diseases. Identifying the endocardial borders of the left ventricle can be difficult due to the inherent properties of ultrasound.

This study proposes a 4D segmentation algorithm that segments over temporal 3D volumes that has minimal user interaction and is based on a diffeomorphic registration approach. In contrast to several existing algorithms, the proposed method does not depend on training data or make any geometrical assumptions. The algorithm was evaluated on seven patients obtained from the Mazankowski Alberta Heart Institute, Edmonton, Canada in comparison to expert manual segmentation.

The proposed approach yielded Dice scores of 0. The corresponding Hausdorff distance values were 4. These results demonstrate that the proposed 4D segmentation approach for the left ventricle is robust and can potentially be used in clinical practice. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium LA imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio.

As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically.