scholarly journals A Myocardial Segmentation Method Based on Adversarial Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tao Wang ◽  
Juanli Wang ◽  
Jia Zhao ◽  
Yanmin Zhang
Keyword(s):  
Blood Vessels ◽  
Heart Defects ◽  
Patient Specific ◽  
Adversarial Learning ◽  
Low Contrast ◽  
Discriminant Model ◽  
Thin Walled Structures ◽  
Improved Performance ◽  
Real World Datasets ◽  
Myocardial Segmentation

Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary wildly, with mild case patients having no obvious symptoms and serious cases being potentially life-threatening. Using cardiovascular magnetic resonance imaging (CMRI) technology to create a patient-specific 3D heart model is an important prerequisite for surgical planning in children with CHD. Manually segmenting 3D images using existing tools is time-consuming and laborious, which greatly hinders the routine clinical application of 3D heart models. Therefore, automatic myocardial segmentation algorithms and related computer-aided diagnosis systems have emerged. Currently, the conventional methods for automatic myocardium segmentation are based on deep learning, rather than on the traditional machine learning method. Better results have been achieved, however, difficulties still exist such as CMRI often has, inconsistent signal strength, low contrast, and indistinguishable thin-walled structures near the atrium, valves, and large blood vessels, leading to challenges in automatic myocardium segmentation. Additionally, the labeling of 3D CMR images is time-consuming and laborious, causing problems in obtaining enough accurately labeled data. To solve the above problems, we proposed to apply the idea of adversarial learning to the problem of myocardial segmentation. Through a discriminant model, some additional supervision information is provided as a guide to further improve the performance of the segmentation model. Experiment results on real-world datasets show that our proposed adversarial learning-based method had improved performance compared with the baseline segmentation model and achieved better results on the automatic myocardium segmentation problem.

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

Optimal Feature Selection and Extraction for Eye Disease Diagnosis

2019 ◽  
pp. 100-112
Author(s):  
Alli P. ◽  
S. K. Somasundaram
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Blood Vessels ◽  
Disease Diagnosis ◽  
Eye Disease ◽  
Fundus Images ◽  
Low Contrast ◽  
Performance Factors ◽  
Time Sensitivity ◽  
Retinal Fundus

Ophthalmologists utilize retinal fundus images of humans for the detection, diagnosis, and prediction of many eye diseases. Automatic scrutiny of fundus images are foremost apprehension for ophthalmologists and investigators. The manual recognition of blood vessels is most deceptive because the blood vessels in a fundus image are multifaceted and with low contrast. Unearthing of blood vessels proffers information on pathological transformation and can smooth the progress of rating diseases severity or mechanically diagnosing the diseases. The manual recognition method turns out to be annoying. Consequently, the automatic recognition of blood vessels is also more significant. For extracting the vessel in fundus images unswerving and habitual methods are obligatory. The proposed methodology is designed to effectively diagnose the eye disease by performing feature extraction succeeded by feature selection and to improve the performance factors such as feature extraction ratio, feature selection time, sensitivity, and specificity when compared to the state-of-art methods.

scholarly journals Efficient finite element hyperelasticity solver for blood vessel simulations

2020 ◽  
Vol 14 ◽  
pp. 174830262093101
Author(s):  
Xinhong Wang ◽  
Zhengzheng Yan ◽  
Yi Jiang ◽  
Rongliang Chen
Keyword(s):  
Finite Element ◽  
Blood Vessel ◽  
Blood Vessels ◽  
High Performance ◽  
Soft Tissues ◽  
Circulatory System ◽  
Numerical Algorithms ◽  
External Pressure ◽  
Patient Specific ◽  
Schwarz Method

The blood vessels play a key role in the human circulatory system. As a tremendous amount of efforts have been devoted to develop mathematical models for investigating the elastic behaviors of human blood vessels, high performance numerical algorithms aiming at solving these models have attracted attention. In this work, we present an efficient finite element solver for an elastodynamic model which is commonly used for simulating soft tissues under external pressure loadings. In particular, the elastic material is assumed to satisfy the Saint–Venant–Kirchhoff law, the governing equation is spatially discretized by a finite element method, and a fully implicit backward difference method is used for the temporal discretization. The resulting nonlinear system is then solved by a Newton–Krylov–Schwarz method. It is the first time to apply the Newton–Krylov–Schwarz method to the Saint–Venant–Kirchhoff model for a patient-specific blood vessel. Numerical tests verify the efficiency of the proposed method and demonstrate its capability for bioengineering applications.

scholarly journals Segmentation of Retinal Blood Vessels Based on Cake Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xi-Rong Bao ◽  
Xin Ge ◽  
Li-Huang She ◽  
Shi Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
False Positive Rate ◽  
New Method ◽  
Real Component ◽  
Low Contrast ◽  
Small Vessels ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Positive Rate

Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate.

scholarly journals When the disease goes beyond the skin, changes in the skin can be a sign of systemic disease

2020 ◽  
Vol 19 (3) ◽  
pp. 95-104
Author(s):  
L. A. Khachatryan ◽  
D. M. Nikolaeva ◽  
A. P. Shcherbakov
Keyword(s):  
Blood Vessels ◽  
Heart Defects ◽  
Systemic Disease ◽  
Neck Region ◽  
Coarctation Of The Aorta ◽  
Infantile Hemangioma ◽  
Internal Organs ◽  
Skin Changes ◽  
Arterial Blood ◽  
Cranial Fossa

Infantile hemangioma may be accompanied by malformations of internal organs and blood vessels. In 1996 PHACE syndrome was defined as a disease which is characterized by the association of segmental infantile hemangioma with localization in the head/neck region and the presence of malformations in the posterior cranial fossa, abnormalities of arterial blood vessels including coarctation of the aorta, heart defects, as well as malformations of the eyes and central nervous system. This article presents a clinical case of a child who was diagnosed this syndrome at the age of 1.5 months based on the presence of segmental hemangioma, as well as large and small criteria specific for this disease. In addition to the main symptoms, the child had an accompanying pathology: Wolff–Parkinson–White syndrome. Therapy with non-selective b-blockers and polychemotherapy allowed stopping already developed and prevent possible complications associated with this syndrome. Parents gave their consent to use information about the child, including fotos, in the article.

scholarly journals Adversarial Energy Disaggregation

2021 ◽  
Vol 2 (4) ◽  
pp. 1-16
Author(s):  
Zhekai Du ◽  
Jingjing Li ◽  
Lei Zhu ◽  
Ke Lu ◽  
Heng Tao Shen
Keyword(s):  
Energy Efficiency ◽  
Data Science ◽  
Adversarial Learning ◽  
Typical Application ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
Novel Method ◽  
Real World Datasets ◽  
Sustainable Energy Policy ◽  
Non Negative Matrix Factorization

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and the hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this article, we propose a novel method named adversarial energy disaggregation based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shared representations for different appliances but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.

scholarly journals On the Estimation of Treatment Effect with Text Covariates

2019 ◽  
Author(s):  
Liuyi Yao ◽  
Sheng Li ◽  
Yaliang Li ◽  
Hongfei Xue ◽  
Jing Gao ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Treatment Effect ◽  
Potential Outcomes ◽  
Estimation Bias ◽  
Matching Method ◽  
Adversarial Learning ◽  
Treatment Effect Estimation ◽  
Real World Datasets ◽  
Effect Estimation ◽  
Open Question

Estimating the treatment effect benefits decision making in various domains as it can provide the potential outcomes of different choices. Existing work mainly focuses on covariates with numerical values, while how to handle covariates with textual information for treatment effect estimation is still an open question. One major challenge is how to filter out the nearly instrumental variables which are the variables more predictive to the treatment than the outcome. Conditioning on those variables to estimate the treatment effect would amplify the estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out the information related to nearly instrumental variables when learning the representations, and then it performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce the bias of treatment effect estimation, which is demonstrated by our experimental results on both semi-synthetic and real-world datasets.

scholarly journals Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhike Zhang ◽  
Shuixin Zhang ◽  
Hongyu Feng
Keyword(s):  
Blood Vessels ◽  
Input Signal ◽  
Medical Images ◽  
Vessel Segmentation ◽  
New Method ◽  
Geometric Transformation ◽  
High Contrast ◽  
Low Contrast ◽  
Resonance Response ◽  
Local Image

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

scholarly journals Quantitative Assessment of 3D Printed Blood Vessels Produced with J750™ Digital Anatomy™ for Suture Simulation

2022 ◽  
Author(s):  
Stefania Marconi ◽  
Valeria Mauri ◽  
Erika Negrello ◽  
Luigi Pugliese ◽  
Andrea Pietrabissa ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Soft Tissues ◽  
Surgical Simulation ◽  
Patient Specific ◽  
Morphological Aspects ◽  
Pathological Conditions ◽  
Surgical Simulations ◽  
Training Of Surgeons ◽  
3D Printed ◽  
Digital Anatomy

Blood vessels anastomosis is one of the most challenging and delicate tasks to learn in many surgical specialties, especially for vascular and abdominal surgeons. Such a critical skill implies a learning curve that goes beyond technical execution. The surgeon needs to gain proficiency in adapting gestures and the amount of force expressed according to the type of tissue he/she is dealing with. In this context, surgical simulation is gaining a pivotal role in the training of surgeons, but currently available simulators can provide only standard or simplified anatomies, without the chance of presenting specific pathological conditions and rare cases. 3D printing technology, allowing the manufacturing of extremely complex geometries, find a perfect application in the production of realistic replica of patient-specific anatomies. According to available technologies and materials, morphological aspects can be easily handled, while the reproduction of tissues mechanical properties still poses major problems, especially when dealing with soft tissues. The present work focuses on blood vessels, with the aim of identifying – by means of both qualitative and quantitative tests – materials combinations able to best mimic the behavior of the biological tissue during anastomoses, by means of J750™ Digital Anatomy™ technology and commercial photopolymers from Stratasys. Puncture tests and stitch traction tests are used to quantify the performance of the various formulations. Surgical simulations involving anastomoses are performed on selected clinical cases by surgeons to validate the results. A total of 37 experimental materials were tested and 2 formulations were identified as the most promising solutions to be used for anastomoses simulation. Clinical applicative tests, specifically selected to challenge the new materials, raised additional issues on the performance of the materials to be considered for future developments.

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