Active Segmentation Research Articles

The Cup Whisperer

The Cup Whisperer

A mathematical model of wound healing in bovine corneal endothelium

We developed a mathematical model to describe healing processes in bovine corneal endothelial (BCE) cells in culture, triggered by mechanical wounds with parallel edges. Previous findings from our laboratory show that, in these cases, BCE monolayers exhibit an approximately constant healing velocity. Also, that caspase-dependent apoptosis occurs, with the fraction of apoptotic cells increasing with the distance traveled by the healing edge. In addition, in this study we report the novel findings that, for wound scratch assays performed preserving the basal extracellular matrix: i) the healing cells increase their en face surface area in a characteristic fashion, and ii) the average length of the segments of the cell columns actively participating in the healing process increases linearly with time. These latter observations preclude the utilization of standard traveling wave formalisms to model wound healing in BCE cells. Instead, we developed and studied a simple phenomenological model based on a plausible formula for the spreading dynamics of the individual healing cells, that incorporates original evidence about the process in BCE cells. The model can be simulated to: i) obtain an approximately constant healing velocity; ii) reproduce the profile of the healing cell areas, and iii) obtain approximately linear time dependences of the mean cell area and average length of the front active segments per column. In view of its accuracy to account for the experimental observations, the model can also be acceptably employed to quantify the appearance of apoptotic cells during BCE wound healing. The strategy utilized here could offer a novel formal framework to represent modifications undergone by some epithelial cell lines during wound healing.

DDDR-18. COMPREHENSIVE PROTEOMIC PROFILING OF BRAFV600E GLIOMA CELLS SHOWS UPREGULATION OF GLYCOLYSIS AND TCA CYCLE PROTEINS AFTER COMBINED MTOR AND MAP KINASE PATHWAY BLOCKADE

Abstract Low-grade gliomas (LGG) are slow-growing tumors that affect neurologic function by pressing on the surrounding parts of the brain. Pediatric low-grade glioma (pLGG) is the most common childhood brain tumors. Patients with pLGG often have a significant life-long disabilities. The most common mutations in pLGG are in the mitogen-activated protein kinase (MAPK) pathway including tumor suppressor neurofibromin (NF1), and the oncogene BRAF. BRAF activates MEK1/2, which activates of ERK1/2, leading to tumorigenesis. BRAFV600E mutation results in the activation segment of the kinase “monomeric form of BRAF”, leading to constitutive activation of MAP kinase and mTOR pathways Combination treatment of TAK228 (mTOR inhibitor) and trametinib (MEK1/2 inhibitor), suppressed both mTOR and MAP kinase pathways and inhibited BRAFV600E glioma tumor growth. To further investigate and understand the molecular mechanism of TAK228 and trametinib combinatorial efficacy, we performed comprehensive proteomic analysis of BRAFV600E glioma tumors treated with TAK228 and trametinib. We found increased levels of many proteins in the glycolysis and TCA cycle. Specifically we found increases at the protein level in citrate synthase, aconitase and malate dehydrogenase in the TCA cycle. In glycolysis, we observed increased glucose transporter expression (SLC2A1), increased hexokinase 2, and increased GAPDH. Upregualtion of these proteins may represent attempts to bypass the effects of mTOR and MAP kinase blockade on the activity of these pathways. We are currently performing flux metabolic profiling using uniformly labeled glucose to assess the activity of the pathways after combined mTOR and MAP kinase blockade. Our results may increase our understanding of how glioma cells use glucose and how mTOR and MAP kinase pathway inhibition affects basic metabolic pathways in glioma cells.

Active shape model registration of ocular structures in computed tomography images

Purpose. The goal of this work is to create an active shape model segmentation method based on the statistical shape model of five regions of the globe on computed tomography (CT) scans and to use the method to categorize normal globe from globe injury. Methods. A set of 78 normal globes imaged with CT scans were manually segmented (vitreous cavity, lens, sclera, anterior chamber, and cornea) by two graders. A statistical shape model was created from the regions. An active shape model was trained using the manual segmentations and the statistical shape model and was assessed using leave-one-out cross validations. The active shape model was then applied to a set of globes with open globe injures, and the segmentations were compared to those of normal globes, in terms of the standard deviations away from normal. Results. The active shape model (ASM) segmentation compared well to ground truth, based on Dice similarity coefficient score in a leave-one-out experiment: 90.2% ± 2.1% for the cornea, 92.5% ± 3.5% for the sclera, 87.4% ± 3.7% for the vitreous cavity, 83.5% ± 2.3% for the anterior chamber, and 91.2% ± 2.4% for the lens. A preliminary set of CT scans of patients with open globe injury were segmented using the ASM and the shape of each region was quantified. The sclera and vitreous cavity were statistically different in shape from the normal. The Zone 1 and Zone 2 globes were statistically different than normal from the cornea and anterior chamber. Both results are consistent with the definition of the zonal injuries in OGI. Conclusion. The ASM results were found to be reproducible and accurately correlated with manual segmentations. The quantitative metrics derived from ASM of globes with OGI are consistent with existing medical knowledge in terms of structural deformation.

Correction to: A hybrid active contour image segmentation model with robust to initial contour position

Correction to: A hybrid active contour image segmentation model with robust to initial contour position

Lev P. Karsavin on the Phenomenology of Revolution

ABSTRACT This article attempts to analyze Karsavin’s theory of revolution in the broader context of a Russian metaphysics of revolution in order to determine the place of Karsavin’s phenomenology of revolution both in his work and within Eurasianist ideology. His article “Phenomenology of Revolution” ontologically links two key concepts within Karsavin’s understanding: the “symphonic person” and the “ruling stratum.” The meaning of revolution consists in leading the symphonic person to a realization of its main tasks, which require the utmost exertion and are related to the very existence of its historical form. This crisis can result in the person’s death, but in the death of the old person is born a new individuation of a higher order of personhood for whom that revolution has been a rebirth. If the symphonic person succeeds in “being revived through death or recovery,” then this will also be the birth of a new ruling stratum and a new government, that is, its new being as a state. Karsavin thus clarifies the historical functions both of the symphonic person and of the ruling stratum. The latter turns out to be a kind of entelechy of the symphonic person, providing its own personal content to the generic concept. The concept of the ruling stratum is explained in the metaphysical context of the symphonic-person theory. In light of the symphonic person’s mission, the ruling stratum is an organic connection among individuals of the active sociocultural segment of the population that has resonated with the spirit of history and the people. In this case, the stratum “rules” along with parties and institutions, eventually passing judgment on them. The main results of Karsavin’s work are (1) modeling a developmental phase of revolution and (2) the concept of the ruling stratum, which allows us to avoid formal sociological understanding of the active elite and direct our attention to the connection between personal activity and the “silent” but by no means “sleeping” substrate of the nation that manifests itself in critical epochs.

COMPARISON MEASUREMENT AND CALCULATION OF BRAIN TUMOR IN MRI MODALITIES UTILISING SPIN-ECHO PULSE SEQUENCE T1-WEIGHTED CONTRAST AND DIGITAL IMAGE PROCESSING APPLICATIONS

Background: Evaluation of brain tumor MRI image results performed by radiologists employing the linear measurement method has several weaknesses and is sensitive to subjectivity. Purpose: To compare the results of measurements and calculations of brain tumors utilizing the linear measurement method on the Siemens 1.5 Tesla MRI modality employing pulse sequence spin echo with T1 contrast weighting compared with the results of measurements and calculations of brain tumors utilizing the active contour segmentation method. Method: An experimental study was conducted on 32 MRI images. Result: The study’s findings indicated that the linear measurement was more significant than the active contour segmentation method (p-value<0,05). The results were obtained by calculating the sensitivity and specificity values of the diagnostic test, which were calculated to be 87.5%. Conclusion: The active contour segmentation method applied to pulse sequence spin-echo T1-weighted contrast can be utilized as an alternative measurement and calculation of brain tumors with a sensitivity and specificity value of 87.5%. Further research suggests developing a Matlab application to compare the results of measurements and calculations of brain tumors on acquiring 3D image magnetic resonance imaging data.

IEEE EMBS International Student Conferences -Insights From 2022 Editions.

Student members within IEEE Engineering in Medicine and Biology Society (EMBS) are one of the most active segments among all other membership levels. Student-led initiatives all around the world have shown the necessity to give students the opportunity to present solutions to educational challenges, aiming to make the learning of young people an enriching and continuous experience while honing their organizational skills. IEEE EMBS SAC [1], formed under vice president for member and student activities, has taken the responsibility to initiate and implement programs for undergraduate and graduate student members of the society. One of these programs, IEEE EMBS ISC, is the flagship event under the oversight of the Professional Development Portfolio. The purpose of the ISCs is to help students learn to manage an IEEE-style conference in a low-pressure environment and improve on their soft skills like leadership, communication, teamwork, and project management. Moreover, it gives them a platform to practice giving and receiving peer feedback on scientific writing and presentations, as well as making international connections which could turn into future collaborations.

Perioperative Management of Pediatric Crohn's Disease.

Crohn's disease (CD) is a chronic inflammatory condition that may affect any part of the gastrointestinal tract. It is transmural in nature and complications such as fistula, strictures and peri-anal disease may occur. There is more extensive anatomic involvement (50% ileocolonic, 20% colonic, 30% with upper GI symptoms) and a higher surgical risk in pediatrics than adults (50% vs 14% by age 30). Studies have shown 3.4%-7.9% of patients require surgery in first year after diagnosis, 13.8-47.2% by 5 years and 28%-35% by 10 years (1). There are three major surgical categories: ileocecal resection, treatment of complication, (fistula/stricture-figure 1,2) or salvage procedure for severe refractory disease. In addition, surgery may be considered with active short segment disease or children illustrating poor growth despite optimized medical therapy (ESPGHAN). Unfortunately, much data on pediatrics is derived from single centers rather than population-based cohorts and the most recent societal reports were based on studies with minimal biologic use (18-24%) (1). Despite larger use of biologics, resection is still needed in 20%- 34%, thus effective peri-operative management of pediatric CD is imperative (1).

Genome-wide identification and expression analysis of BrAGC genes in Brassica rapa reveal their potential roles in sexual reproduction and abiotic stress tolerance.

AGC protein kinases play important roles in regulating plant growth, immunity, and cell death. However, the function of AGC in Brassica rapa has not yet been clarified. In this study, 62 BrAGC genes were identified, and these genes were distributed on 10 chromosomes and divided into six subfamilies. Analysis of gene structure and conserved motifs showed that the activation segment of BrAGC genes was highly conserved, and genes of the same subfamily showed higher sequence and structural similarity. Collinearity analysis revealed that BrAGCs were more closely related to AtAGCs than to OsAGCs. Expression profile analysis revealed that BrAGCs were preferentially expressed in flowers and BrAGC26, BrAGC33, and BrAGC04 were preferentially expressed in the stigma; the expression of these genes was significantly upregulated after self-incompatibility pollination, and the expression of BrAGC13 and BrAGC32 was significantly upregulated after cross-pollination. In addition, several typical cis-elements involved in the stress response were identified in BrAGC promoters. The expression levels of BrAGC37 and BrAGC44 significantly varied under different types of abiotic stress. Collectively, we identified that BrAGC26, BrAGC33, and BrAGC44 have the greatest potential in regulating pollen-pistil interaction and abiotic stress tolerance, respectively. Our findings will aid future functional investigations of BrAGCs in B. rapa.

Zymogenic latency in an ∼250-million-year-old astacin metallopeptidase.

The horseshoe crab Limulus polyphemus is one of few extant Limulus species, which date back to ∼250 million years ago under the conservation of a common Bauplan documented by fossil records. It possesses the only proteolytic blood-coagulation and innate immunity system outside vertebrates and is a model organism for the study of the evolution and function of peptidases. The astacins are a family of metallopeptidases that share a central ∼200-residue catalytic domain (CD), which is found in >1000 species across holozoans and, sporadically, bacteria. Here, the zymogen of an astacin from L. polyphemus was crystallized and its structure was solved. A 34-residue, mostly unstructured pro-peptide (PP) traverses, and thus blocks, the active-site cleft of the CD in the opposite direction to a substrate. A central `PP motif' (F35-E-G-D-I39) adopts a loop structure which positions Asp38 to bind the catalytic metal, replacing the solvent molecule required for catalysis in the mature enzyme according to an `aspartate-switch' mechanism. Maturation cleavage of the PP liberates the cleft and causes the rearrangement of an `activation segment'. Moreover, the mature N-terminus is repositioned to penetrate the CD moiety and is anchored to a buried `family-specific' glutamate. Overall, this mechanism of latency is reminiscent of that of the other three astacins with known zymogenic and mature structures, namely crayfish astacin, human meprin β and bacterial myroilysin, but each shows specific structural characteristics. Remarkably, myroilysin lacks the PP motif and employs a cysteine instead of the aspartate to block the catalytic metal.

A coarse-to-fine segmentation frame for polyp segmentation via deep and classification features

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colon cancer. Deep convolution network can extract the common high level features of the target. However, most network models ignore some individual features, so the sample prediction results in complex space are fuzzy and lack of regularity. In this paper, a coarse-to-fine segmentation frame for polyp segmentation via deep and classification features is proposed. Firstly, batch schatten-p norms maximization is introduced into a network model to strengthen the predict map. Then, an automatic two classification mechanism is constructed and the prediction map is classified into two categories: simple and complex samples. Since the CNN prediction maps of simple samples are close to binary images, the prediction maps are not processed. Finally, an active contour model segmentation algorithm for saliency detection of complex samples is proposed. Experiments on Kvasir-SEG, CVC-300, CVC-ClinincDB, CVC-ColonDB and ETIS-LaribPolypDB datasets using multiple models verify the effectiveness of the framework. Code is available at https://doi.org/10.24433/CO.7821162.v1.

Optical disk segmentation in human retina images with golden eagle optimizer

In ophthalmology, examination of the optic nerve and disc of the retina is a common way to diagnose a variety of eye diseases such as aging, diabetes, optic neuritis, glaucoma, papilledema, and ischemic optic neuropathy. This study presents the implementation of three combined methods based on the Golden Eagle algorithm, backup vector machine and geometrically active contour method and image segmentation. These methods have been tested using the well-known retina database benchmark, RIM-ONE. This study uses preprocessing based on the geometrically active Shannon contour guided by the Golden Eagle algorithm and post-processing based on regular spaced cross-section segmentation. The Golden Eagle method is more efficient than approaches such as the geometrically active contour and the SVM method. The proposed method, compared to other methods, shows higher mean values in terms of image similarity and statistical index. It is confirmed that the proposed Golden Eagle method could be used in the future to assess the clinical fit of retinal images.

Optimized active contor segmentation model for medical image compression

Nowadays, medical imaging systems tend to have greatest impact on disease identification, diagnosis, and surgical preparation. To save hardware space and transmission bandwidth, it is important to reduce data redundancy in the image. Compressing images is very important for storage and transmitting purposes as it decreases the amount of bits required while retaining the critical information content encapsulated in the image document. This makes the system more peculiar in this field. The proposed paradigm image segmentation is the first step attained by the Optimized Active Contour Model (OACM). Using a new Modified marriage in honey bees optimization model (MMBO), ACM's weighting factor and maximum iteration are fine-tuned. Thereby, the collected inputimage is segmented into N-ROI and ROI. The ROI marked field will indeed be encoded using ISPIHT-based lossy compression model, whereas the non-ROI area is encoded using DCT based lossy compression model. In terms of BSC, the outcomes from both ISPIHT algorithm and DCTmodel are merged and the compressed image is itsoutput. Then, the compressed image will then be subjected to image decompression. This will include bit-stream segregation, which will be processed separately for the ROI and non-ROI regions using ISPIHT decoder and DCT-based decomposition. This process results in the original image. A comparative evaluation is undergone between the proposed and the existing techniques under PSNR, SSIM, and CR. Accordingly, the PSNR of the Proposed model is (∼)0.22, which is 26 %, 50 %, 60 %, and 55 % superior to the conventional methods like the MFO, LA, MBO, and JCF-LA.

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

A multi-source information fusion-based disease class classification of stroke patients was implemented to address the low classification accuracy of pure input motion and electromyographic signals. sEMG sensor MYO arm ring and wearable wireless motion sensor Shimmer were used as data acquisition devices. The Butterworth high-pass filter filtering and envelope thresholding method detected the activity segment. Detection and FIR filtering using the window function method remove interference from the motion signal. A weighted cross-validation-based feature selection (W-CVFS) method is proposed for feature fusion selection. The top 10 features selected by the W-CVFS method and all 18 features are input to the deep neural network for training and testing, and the feature classification result of the W-CVFS method is 79.17%, which is better than the existing mRMR method (66.67%) and ILFS method (62.50%). The classificatip9uon accuracy of multi-source information fusion was 95.385%, which was higher than that of a single input motion signal or sEMG. The experiments showed that the proposed method can retain the features that have more influence on the classification results and can improve the classification accuracy of the rehabilitation model for stroke patients.

A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method

When the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to extract more texture parts of the image, an adaptively fractional order matrix is constructed according to the gradient information of the image, varying the fractional order of each pixel. Then, the traditional edge-stopping function in the regularization term is susceptible to noise, and a new fractional-order edge-stopping function is designed to improve noise resistance. In this paper, a fitting term based on adaptive fractional differentiation is introduced to solve the problem of improper selection of the initial contour position leading to inaccurate segmentation results so that the initial contour position can be selected arbitrarily. Finally, the experimental results show that the proposed method can effectively improve the segmentation accuracy of noise images and weak-edge images and can arbitrarily select the position selection of the initial contour.

CSI-Based Human Continuous Activity Recognition Using GMM–HMM

Recently, device-free human activity recognition has become a research hotspot, and great progress has been made in ubiquitous computing. Among the different kinds of implementations, activity recognition based on WiFi channel state information (CSI) has attracted enormous attention for its superiority compared with conventional approaches. In this article, a device-free human continuous activity recognition system based on WiFi CSI is proposed. First, the CSI phase difference expansion matrix is constructed as a more obvious activity recognition feature, and a method based on threshold combined with labeling is used to achieve continuous activity segmentation. Then, the Gaussian mixture model–hidden Markov model (GMM–HMM) is used to model the CSI feature data of each activity, which is originally used for human 3-D skeleton-based activity recognition. The approach is of great value not only for its high accuracy compared with other classification approaches, such as long short-term memory (LSTM) and convolutional neural network (CNN), but also for its tremendous advantage that a pretty short CSI time series could be used to identify human activities, thus saving computer memory, reducing system calculation time greatly, and improving the error tolerance rate of the segmentation. Experiments on measured activity datasets and methods comparison demonstrate the effectiveness and superiority of the proposed system. The factors affecting system performance, such as the length of the CSI time series, have been discussed in this article.

A hybrid active contour image segmentation model with robust to initial contour position

A hybrid active contour image segmentation model with robust to initial contour position

Impact of the Covid-19 Pandemic on Organ Donation and Transplants at Hospital de Base and in the State of São Paulo, Brazil

Abstract: The Covid-19 pandemic had severe impacts on various sectors of society, especially in the area of medicine. In this area, one segment of activity that was greatly impacted was transplantation, which at first had to be halted in numerous centers, for various reasons. The objective of this article was to analyze, in a comparative way between the years 2019 and 2020, the impact of the Covid-19 pandemic in the numbers of transplantations in a district hospital (DH), a national reference in transplantations, and in the state of São Paulo, as well as in the number of donations from these locations. The method used in this work was the comparative analysis using data obtained from the Brazilian Transplant Registry, the Brazilian Association of Organ Transplantation and the State Transplant System. The following organs were analyzed in the DH and in the state of São Paulo: heart, liver, lung and kidney. Pancreas was analyzed only in the state. Through the analyses, it was observed that in the DH there was a decrease in the numbers of transplantations for all organs analyzed, and in the state of São Paulo, in the numbers of lung and kidney transplantations, maintaining the number of pancreas transplantations and increasing the numbers of heart and liver transplantations. Regarding donors, there was a decrease in the number of potential donors, but an increase in the number of actual donors, due to a decrease in the family refusal rate.

Recording of pig neuronal activity in the comparative context of the awake human brain

Gyriform mammals display neurophysiological and neural network activity that other species exhibit only in rudimentary or dissimilar form. However, neural recordings from large mammals such as the pig can be anatomically hindered and pharmacologically suppressed by anesthetics. This curtails comparative inferences. To mitigate these limitations, we set out to modify electrocorticography, intracerebral depth and intracortical recording methods to study the anesthetized pig. In the process, we found that common forms of infused anesthesia such as pentobarbital or midazolam can be neurophysiologic suppressants acting in dose-independent fashion relative to anesthetic dose or brain concentration. Further, we corroborated that standard laboratory conditions may impose electrical interference with specific neural signals. We thus aimed to safeguard neural network integrity and recording fidelity by developing surgical, anesthesia and noise reduction methods and by working inside a newly designed Faraday cage, and evaluated this from the point of view of neurophysiological power spectral density and coherence analyses. We also utilized novel silicon carbide electrodes to minimize mechanical disruption of single-neuron activity. These methods allowed for the preservation of native neurophysiological activity for several hours. Pig electrocorticography recordings were essentially indistinguishable from awake human recordings except for the small segment of electrical activity associated with vision in conscious persons. In addition, single-neuron and paired-pulse stimulation recordings were feasible simultaneously with electrocorticography and depth electrode recordings. The spontaneous and stimulus-elicited neuronal activities thus surveyed can be recorded with a degree of precision similar to that achievable in rodent or any other animal studies and prove as informative as unperturbed human electrocorticography.