MATLAB-based Software Research Articles

ANGI-12. THE IMPACT OF STANDARD-OF-CARE TREATMENT ON THE RELATIONSHIP BETWEEN CELL DENSITY AND SOX2 POSITIVITY IN GLIOBLASTOMA PATIENTS AT AUTOPSY

Abstract INTRODUCTION Sex-determining region Y-box 2 (SOX2) staining highlights pluripotent cells and provides an early sign of tumor cell invasion in glioblastoma patients. This infiltration can often occur at the single cell level and can occur in the absence of lower resolution histological tumor features such as hypercellularity. This study assessed the impact of standard treatment regimens on the relationship between cellularity and SOX2+% at death using autopsy samples as ground truth. METHODS A total of 36 IDH-wildtype glioblastoma patients were included for this study (23M/13F, mean age=61.9). Tissue samples from both suspected tumor invasion and suspected normal tissue were collected for each patient, which were then stained for SOX2, digitized at 40X resolution, and segmented into positive staining nuclei percentage using a color deconvolution algorithm. These samples were then aligned to the MRI using in-house MATLAB-based software, where manually defined control points placed on areas of architectural similarity are used to compute a transform that corrects for nonlinear shrinkage of the tissue sample due to post-mortem processing. The association between SOX2+% and cell density was then computed per subject, and compared between patients who received SOC (radiation and temozolomide) treatment following surgery (as well as additional treatment per case, n=31) and patients who opted for no additional treatment following surgery (n=5). RESULTS Patients who received no additional treatment showed substantially higher correlation between cell density and SOX2+% than patients who had received SOC (t=2.90, p=0.006). No association with overall survival was observed (r=0.17, p=0.329). CONCLUSIONS These results indicate a decoupling between hypercellularity and SOX2+% associated with standard GBM treatments, indicating that radiation and other treatments induce a more heterogenous tumor microenvironment that could lead to single-cell tumor invasion beyond gross histological markers of tumor presence.

Abstract We015: Modeling Cardiac Arrhythmogenicity of hiPSC-CMs and Cardiac Fibroblasts Nanopatterned Coculture and Machine Learning

Background: Sudden cardiac death is one of the most threatening heart conditions in the U.S. Most individuals have an underlying structural cardiac disease associated with cardiac fibrosis and ventricular arrhythmia. The relationship between fibrosis and arrhythmias is found to be related to cardiovascular cell couplings, especially cardiomyocytes (CMs) and cardiac fibroblasts (CFs). While animal models have been applied to model cardiac arrhythmias, the disparities between animal models and humans limit the accuracy of pro-arrhythmic predictions. The use of human induced pluripotent stem cells (hiPSCs)-derived CMs has permitted highly modular testing conditions, specifically the ratio of CFs to CMs in an engineered coculture system. Moreover, machine learning (ML) has become an emerging tool in cardiology, such as cardiac toxicity and function evaluation and classification, with its unique advantages of high accuracy and efficiency with less human bias and intuition. Hypothesis: We hypothesize that cardiac fibrosis-associated arrhythmia will be modeled by nanopatterned coculture of hiPSC-CMs and CFs at varied ratios, and the arrhythmic samples will be accurately and efficiently classified by ML algorithms. Approach: The hiPSC-CMs were cocultured and nanopatterned with CFs at ratios of 0 to 30% for 7 days. Upon the electrical pacing at 1Hz, the action potential was recorded by live-cell optical mapping with FluoVolt. The recorded membrane potential and electrical propagation were analyzed by Matlab-based Electomap software. Results: The cocultured hiPSC-CMs and CFs were aligned with aligned sarcomeres and uni-directional contraction ( Fig. 1a ). The cardiac arrhythmia was observed in 15% CFs with spiral wave ( Fig. 1b ) in comparison to the hiPSC-CMs with 0% CF. Beat-to-beat and depolarization durations were found significant with top ranks in Feature Importance to facilitate the distinction between arrhythmic and non-arrhythmic samples by the unsupervised K-means Clustering ( Figs. 1c and d ). Conclusions: We have successfully established a nanopatterned coculturing system of hiPSC-CMs and CFs for modeling cardiac fibrosis-induced arrhythmia, which was further analyzed and classified by ML algorithms. This system reveals great potential for better understanding the relationship between cardiac fibrosis-associated arrhythmia and sudden cardiac death and also drug evaluation for anti-cardiac arrhythmia.

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system

Objective. Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach. Approach. The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation. Main results. The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be 1.5±2.1 mm and −0.6±2.2 mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach. Significance. The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.

M_IFCB: a MATLAB-based software for multi‑GNSS inter‑frequency clock bias estimation and forecast

M_IFCB: a MATLAB-based software for multi‑GNSS inter‑frequency clock bias estimation and forecast

GravNetAdj: a MATLAB-based data processing software for gravity network adjustment

GravNetAdj: a MATLAB-based data processing software for gravity network adjustment

GTS_CME: an open-source MATLAB-based software for the analysis of common mode errors in GNSS coordinate time series

GTS_CME: an open-source MATLAB-based software for the analysis of common mode errors in GNSS coordinate time series

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the structural health monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the earthquake early warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time (RT) mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in RT mode was investigated in the frequency and time domains, accepting the displacements from the linear variable differential transformer sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in RT with the SF-VA technique.

The effect of MATLAB-based metal artifact reduction software on radiotherapy dose distribution

The effect of MATLAB-based metal artifact reduction software on radiotherapy dose distribution

Comparative Study of Spheroids (3D) and Monolayer Cultures (2D) for the In Vitro Assessment of Cytotoxicity Induced by the Mycotoxins Sterigmatocystin, Ochratoxin A and Patulin.

Mycotoxins are secondary metabolites produced by filamentous fungi associated with a variety of acute and chronic foodborne diseases. Current toxicology studies mainly rely on monolayer cell cultures and animal models, which are undeniably affected by several limitations. To bridge the gap between the current in vitro toxicology approach and the in vivo predictability of the data, we here investigated the cytotoxic effects induced by the mycotoxins sterigmatocystin (STE), ochratoxin A (OTA) and patulin (PAT) on different 2D and 3D cell cultures. We focused on human tumours (neuroblastoma SH-SY5Y cells and epithelial breast cancer MDA-MB-213 cells) and healthy cells (bone marrow-derived mesenchymal stem cells, BM-MSC, and umbilical vein endothelial cells, HUVECs). The cytotoxicity of STE, OTA, and PAT was determined after 24, 48 and 72 h of exposure using an ATP assay in both culture models. Three-dimensional spheroids' morphology was also analysed using the MATLAB-based open source software AnaSP 1.4 version. Our results highlight how each cell line and different culture models showed specific sensitivities, reinforcing the importance of using more complex models for toxicology studies and a multiple cell line approach for an improved and more comprehensive risk assessment.

The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

Pulmonary diseases, notably lung cancer and valley fever, pose significant health risks. Therefore, a swift and accurate diagnostic tool is imperative. This study sought to harness advanced technologies, integrating computer vision techniques with computed tomography (CT) scans, to differentiate between lung cancer and valley fever, thereby enhancing diagnostic precision. We hypothesized that the MATLAB-based software tool developed would discern minute and distinct features of lung nodules specific to either lung cancer or valley fever with more precision than traditional diagnostics. Upon use of Otsu's thresholding and object size filtering, distinct morphological features were extracted from binary versions of the images. The MATLAB-based software consistently detected and differentiated lung cancer and valley fever based on the nodules' features, such as solidity, extent, and roundedness, among others. While size wasn't a consistent indicator as per previous studies, features like solidity showed great contrast between the distinct images. Overall, the software demonstrated promising potential in providing features to differentiate between two critical pulmonary diseases. However, a future study is needed with a larger number of images to further validate and refine the prediction algorithm prototype developed in this work, ensuring its accuracy for potential implementation in clinical settings. Once the technique is fully developed, it could reduce the chances of human error and expedite early detection and intervention. Future enhancements may incorporate other imaging modalities, promoting diagnostic capabilities. The study underlines the transformative role of machine learning-based analysis of CT scans in revolutionizing healthcare diagnostics.

TEMsuite – A Matlab-based software platform for TEM data analysis

TEMsuite – A Matlab-based software platform for TEM data analysis

MATLAB-Based Algorithm and Software for Analysis of Wavy Collagen Fibers.

Knowledge of soft tissue fiber structure is necessary for accurate characterization and modeling of their mechanical response. Fiber configuration and structure informs both our understanding of healthy tissue physiology and of pathological processes resulting from diseased states. This study develops an automatic algorithm to simultaneously estimate fiber global orientation, abundance, and waviness in an investigated image. To our best knowledge, this is the first validated algorithm which can reliably separate fiber waviness from its global orientation for considerably wavy fibers. This is much needed feature for biological tissue characterization. The algorithm is based on incremental movement of local regions of interest (ROI) and analyzes two-dimensional images. Pixels belonging to the fiber are identified in the ROI, and ROI movement is determined according to local orientation of fiber within the ROI. The algorithm is validated with artificial images and ten images of porcine trachea containing wavy fibers. In each image, 80-120 fibers were tracked manually to serve as verification. The coefficient of determination R2 between curve lengths and histograms documenting the fiber waviness and global orientation were used as metrics for analysis. Verification-confirmed results were independent of image rotation and degree of fiber waviness, with curve length accuracy demonstrated to be below 1% of fiber curved length. Validation-confirmed median and interquartile range of R2, respectively, were 0.90 and 0.05 for curved length, 0.92 and 0.07 for waviness, and 0.96 and 0.04 for global orientation histograms. Software constructed from the proposed algorithm was able to track one fiber in about 1.1 s using a typical office computer. The proposed algorithm can reliably and accurately estimate fiber waviness, curve length, and global orientation simultaneously, moving beyond the limitations of prior methods.

Photocatalytic Simulation of Phenol Waste Degradation Using Titanium Dioxide (TiO2) P25-Based Photocatalysts

Phenol waste treatment is vital in industries such as polymer production, coal gasification, refinery, and coke production. Photocatalytic technology using semiconductor materials offers an effective and ecofriendly approach to degrade phenol. TiO2 P25 is a widely used photocatalyst, known for its cost-effectiveness, favorable optical and electronic properties, high photoactivity, and photostability. The PHOTOREAC application, a recently developed MATLAB-based software, simulates the degradation of phenol using visible light. A study that combines existing literature and research revealed that pH significantly influences photocatalytic activity, with an optimum pH of 7 for TiO2 P25-mediated phenol degradation. The recommended photocatalyst concentration ranged from 0 to 10 g/L for reactor volumes between 25 and 60 mL, and from 0 to 5 g/L for 100-mL reactors. Phenol wastewater volume and light intensity also impact degradation efficiency. Adequate oxygen supply, achieved through bubbling and mixing, is essential for the formation of radical compounds. The Ballari kinetic model proved to be the most suitable for phenol degradation with TiO2 P25. Thus, by combining PHOTOREAC simulations with experimental data, the treatment process could be optimized to achieve higher degradation efficiency and estimate the treatment time for specific waste degradation levels. This study contributes to the advancement of phenol waste treatment and the development of improved photocatalytic wastewater treatment technologies.

Optimal Planning Target Margin for Prostate Radiotherapy Based on Interfractional and Intrafractional Variability Assessment during 1.5T MR-Guided Radiotherapy.

MR-guided radiotherapy (MRgRT) provides superior soft-tissue contrast over CT-based image guidance. We collected and analyzed daily pre-treatment (PRE) and real-time motion-monitoring (MM) MR images of patients receiving prostate radiotherapy to assess interfractional and intrafractional variability of prostate using two localization methods: pelvic bony anatomy (bone) and prostate during online adaptive radiotherapy (ART). PRE and MM MRIs for the first five fractions of twenty prostate cancer patients who received definitive MRgRT with 1.5T MRI were collected. Using MIM software, rigid registration between PRE MRI and planning CT images based on pelvic bony anatomy and prostate reproduced bone localization and online ART, respectively. To determine interfractional setup margin (SM), prostate was delineated on all PRE MRIs registered after bone and prostate localizations by a radiation oncologist, and centroid values of prostate contours between planning CT and PRE MRIs were compared. To determine interobserver variability, another radiation oncologist, a medical physicist, and a radiotherapist contoured prostate for both localization methods. For internal margin (IM) assessment, we used MM MRIs of the five patients who had all three sets of coronal, sagittal, and axial cine images and determined the maximum contour displacement using in-house MATLAB-based software converting binary image files to 2D cine images with a superimposed grid of 1 mm spacing. A total of 100 PRE and 25 MM MRIs were analyzed. Four hundred prostate contours were delineated on MR images registered with planning CT based on both bony anatomy and prostate. After bone localization, SM was 0.57±0.42 mm in left-right (LR), 2.45±1.98 mm in anterior-posterior (AP), and 2.28±2.08 mm in superior-inferior (SI) directions, and IO was 1.06±0.58 mm in LR, 2.32±1.08 mm in AP, and 3.30±1.85 mm in SI directions. After prostate localization, SM was 0.76±0.57 mm in LR, 1.89±1.60 mm in AP, and 2.2±1.79 mm in SI directions, and IO was 1.11±0.55 mm in LR, 2.13±1.07 mm in AP, and 3.53±1.65 mm in SI directions. Average IM was 2.12±0.86 mm, 2.24±1.07 mm, and 2.84±0.88 mm in LR, AP, and SI directions, respectively. Using daily MRIs from MRgRT, we showed that movements in the SI direction were the largest source of variability in prostate definitive RT. In addition, interobserver variability was a non-negligible source of margin. Optimal PTV margin should also consider internal margin, especially in the SI direction.

Surface-Based Functional Alterations in Early-Onset and Late-Onset Parkinson's Disease: A Multi-Modal MRI Study.

This study used a surface-based method to investigate brain functional alteration patterns in early-onset Parkinson's disease (EOPD) and late-onset Parkinson's disease (LOPD) to provide more reliable imaging indicators for the assessment of the two subtypes. A total of 58 patients with Parkinson's disease were divided into two groups according to age at onset: EOPD (≤50 years; 16 males and 15 females) and LOPD (>50 years; 17 males and 10 females) groups. Two control groups were recruited from the community: young adults (YC; ≤50 years; 8 males and 19 females) and older adults (OC; >50 years; 12 males and 10 females). No significant differences were observed between the EOPD and YC groups or the LOPD and OC groups in terms of age, sex, education, and MMSE scores (p > 0.05). No statistically significant differences were observed between the EOPD and LOPD groups in terms of education, H-Y scale, UPDRS score, or HAMD score (p > 0.05). Data preprocessing and surface-based regional homogeneity (2D-ReHo) calculations were subsequently performed using the MATLAB-based DPABIsurf software. The EOPD group showed decreased 2D-ReHo values in the left premotor area and right dorsal stream visual cortex, along with increased 2D-ReHo values in the left dorsolateral prefrontal cortex. In patients with LOPD, 2D-ReHo values were decreased in bilateral somatosensory and motor areas and the right paracentral lobular and mid-cingulate. The imaging characterization of surface-based regional changes may serve useful as monitoring indicators and will help to better understand the mechanisms underlying divergent clinical presentations.

Modeling and optimization of synergistic ozone-ultraviolet-chlorine process for reclaimed water disinfection: From laboratory tests to software simulation.

The ozone-ultraviolet (UV)-chlorine process is a highly effective method of disinfection in water reuse system, but currently still lacks precise quantification and accurate control. It is difficult to determine the dosage of each disinfectant because of the complex interactions that occur between disinfection units and the complicated mathematical calculation required. In this study, we proposed a dosage optimization model for ozone-UV-chlorine synergistic disinfection process. The model was able to identify the cost-effective doses of the disinfectants under the constraints of microbial inactivation, decolorization, and residual chlorine retention requirements. Specifically, the simulation of microbial inactivation rates during synergistic disinfection process was accomplished through quantification of the synergistic effects between disinfection units and the introduction of enhancement coefficients. In order to solve this optimization model rapidly and automatically, a MATLAB-based software program with graphical user interface was developed. This software consisted of calibration unit, prediction unit, assessment unit, and optimization unit, and was able to simulate synergistic ozone-UV-chlorine process and identify the optimal dose of ozone, UV, and chlorine. Validation experiments revealed good agreements between the experimental data and the results calculated by the developed software. The developed software is believed to help the water reclamation plants improve disinfection efficiency and reduce the operational costs of synergistic disinfection processes.

Exploration of the pulse pileup effects in a clinical CdTe-based photon-counting computed tomography.

High tube current generates a high flux of x-rays to photon counting detectors (PCDs) that can potentially result in the piling up of pulses formed by concurrent photons, which can cause count loss and energy resolution degradation. To evaluate the performance of clinical photon-counting CT (PCCT) systems in high flux, potentially influenced by pulse pileup effects, in terms of task-generic image quality metrics. A clinical phantom was scanned on a commercial PCCT scanner (NAEOTOM Alpha, Siemens) at 120kV under fourteen different tube current levels (40-1000mA) with a rotation time of 0.25 s and a pitch of 1. The dose levels corresponded to CTDIvol (32cm phantom) of 0.79-19.8mGy. CT sinograms were reconstructed using QIR-off mode (noniterative reconstruction algorithm), Br44 kernel, and a voxel size of . imQuest, an open-source MATLAB-based software package was used to calculate noise power spectrum (NPS), task transfer function (TTF), contrast-to-noise ratio (CNR), and CT number according to AAPM Task Group 233 metrology. The 50% cut-off frequency of TTF (f50 ) remained mostly constant across all higher tube currents for all inserts, namely polyethylene, bone, air, and acrylic. Using the lowest two data points (40 and 80mA), the expected relationship between noise magnitude and tube current was determined to be noise mA-0.47 . The measured noise magnitude were up to 11.1% higher than the expected value at the highest tube current. The average frequency of NPS (fav ) decreased from 0.32 to 0.29mm-1 as tube current increased from 40 to 1000mA. No considerable effects were observed in CT number measurement of any insert; however, CT numbers for air and bone changed almost monotonically as tube current increased. Absolute CNR increased monotonically for all inserts; however, the difference between measured and expected CNRs were approximately -6% to 12% across all tube currents. Increasing tube currents did not affect the spatial resolution, but slightly affected the CT number and noise measurements of the clinical PCCT system. However, the effects were only considerable at clinically irrelevant tube currents used on a small 20-cm phantom. In general clinical practices, automatic exposure control techniques are used to decrease the variation of flux on the detector, which alleviates the chances of detector saturation due to high count rates. The observed effects could be due to pulse pileup, signal-dependent filtration of the system, or nonlinearities in the reconstruction algorithm. In conclusion, either the deadtime of the detector used in the photon-counting CT system is shorter such that count losses due to pulse pileup are negligible, or pulse pileup has inconsiderable effects on the image quality of clinical photon-counting CT systems in routine clinical practice due to possible corrections applied on the system.

DCB estimation and analysis using the single receiver GPS/GLONASS observations under various seasons and geomagnetic activities

Global Navigation Satellite Systems (GNSS) have many applications in surveying, geodesy, and everyday life, so the accuracy of observations is very important. To have accurate results, many errors should be corrected, including ionospheric errors. Estimating the Differential Code Bias (DCB) and Total Electron Content (TEC) is essential to reduce ionospheric errors. This study's main aim is to determine how the combination of GPS (Global Positioning System) and GLONASS (GLObal NAvigation Satellite System) observations affect the estimation of DCB of satellites (SDCB) and receivers (RDCB) under the effects of geomagnetic storms and seasonal changes. The MATLAB-based software GR_DCB (GPS and GLONASS DCB estimation) was developed to estimate the SDCB and RDCB based on GPS and GLONASS observations. Theresults were tested using data from the International GNSS Service (IGS) network from nine stations and compared to previous studies to assess the effectiveness of the developed code. The findings are consistent with IGS products and more precise than previous studies that relied solely on GPS observations. An analysis of the effects of the geomagnetic storm on DCB variance is considered for June 22–23, 2015. During the geomagnetic storm, all GPS and GLONASS stations exhibited a fall in RDCB, led by a modest increase in some stations. The SDCB had no observed change on storm days compared with quiet days. The seasonal change affected GPS and GLONASS stations RDCB. Winter, which has the highest RDCB mean values, is also the season when a deviation in RDCB is the greatest. Autumn and Summer had the lowest dispersion in RDCB over the season and the lowest RDCB values.

A Software Tool for the Measurement of the Aortic Annulus Area by Means of Computed Tomography Image Analysis for the Planning of Transcatheter Aortic Valve Replacement (TAVR).

Cardiovascular diseases (CVD) are the leading cause of death globally, being the heart valve complications one of the five most common heart problems. The aim of this study is the development of a MATLAB-based software tool to obtain several measurements derived from the aortic annulus for the planning of transcatheter aortic valve replacement (TAVR). The proposed software tool utilizes computed tomography (CT) images to reconstruct a volume of the patient. This virtual volume is rotated to situate the images in the plane which cuts the most basal points of the three aortic valve cusps, namely the aortic annulus, and obtain the required measurements. Nevertheless, the computer-user interaction will be entirely based on 2-dimension techniques to reduce both the complexity of the app and the computational load. The program was validated in CT images of 10 subjects with diagnosed aortic stenosis. A comparison of the results with the measurements used in the real clinical practice showed no significant differences between both methods.Clinical Relevance- The resulting computer tool provides significant information about the diseased aortic valve. This will allow clinicians to select the right prosthetic heart valve. It represents a cheaper and less complex alternative to sophisticated software tools which are currently being used to plan the intervention.

MEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.