RESUMO
Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.
Assuntos
Conservação dos Recursos Naturais , Ecossistema , Animais , Biodiversidade , PlantasRESUMO
Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium's broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.
RESUMO
The emergence of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inspired rapid research efforts targeting the host range, pathogenesis and transmission mechanisms, and the development of antiviral strategies. Genetically modified mice, rhesus macaques, ferrets, and Syrian golden hamsters have been frequently used in studies of pathogenesis and efficacy of antiviral compounds and vaccines. However, alternatives to in vivo experiments, such as immortalized cell lines, primary respiratory epithelial cells cultured at an air-liquid interface, stem/progenitor cell-derived organoids, or tissue explants, have also been used for isolation of SARS-CoV-2, investigation of cytopathic effects, and pathogen-host interactions. Moreover, initial proof-of-concept studies for testing therapeutic agents can be performed with these tools, showing that animal-sparing cell culture methods could significantly reduce the need for animal models in the future, following the 3R principles of replace, reduce, and refine. So far, only few studies using animal-derived primary cells or tissues have been conducted in SARS-CoV-2 research, although natural infection has been shown to occur in several animal species. Therefore, the need for in-depth investigations on possible interspecies transmission routes and differences in susceptibility to SARS-CoV-2 is urgent. This review gives an overview of studies employing alternative culture systems like primary cell cultures, tissue explants, or organoids for investigations of the pathophysiology and reverse zoonotic potential of SARS-CoV-2 in animals. In addition, future possibilities of SARS-CoV-2 research in animals, including previously neglected methods like the use of precision-cut lung slices, will be outlined.
Assuntos
COVID-19 , Doenças dos Roedores , Animais , Antivirais/uso terapêutico , COVID-19/veterinária , Cricetinae , Modelos Animais de Doenças , Furões , Pulmão/patologia , Macaca mulatta , Camundongos , Doenças dos Roedores/patologia , SARS-CoV-2RESUMO
BACKGROUND: DNA is a promising storage medium for high-density long-term digital data storage. Since DNA synthesis and sequencing are still relatively expensive tasks, the coding methods used to store digital data in DNA should correct errors and avoid unstable or error-prone DNA sequences. Near-optimal rateless erasure codes, also called fountain codes, are particularly interesting codes to realize high-capacity and low-error DNA storage systems, as shown by Erlich and Zielinski in their approach based on the Luby transform (LT) code. Since LT is the most basic fountain code, there is a large untapped potential for improvement in using near-optimal erasure codes for DNA storage. RESULTS: We present NOREC4DNA, a software framework to use, test, compare, and improve near-optimal rateless erasure codes (NORECs) for DNA storage systems. These codes can effectively be used to store digital information in DNA and cope with the restrictions of the DNA medium. Additionally, they can adapt to possible variable lengths of DNA strands and have nearly zero overhead. We describe the design and implementation of NOREC4DNA. Furthermore, we present experimental results demonstrating that NOREC4DNA can flexibly be used to evaluate the use of NORECs in DNA storage systems. In particular, we show that NORECs that apparently have not yet been used for DNA storage, such as Raptor and Online codes, can achieve significant improvements over LT codes that were used in previous work. NOREC4DNA is available on https://github.com/umr-ds/NOREC4DNA . CONCLUSION: NOREC4DNA is a flexible and extensible software framework for using, evaluating, and comparing NORECs for DNA storage systems.
Assuntos
Algoritmos , DNA , DNA/genética , Armazenamento e Recuperação da Informação , Análise de Sequência de DNA , SoftwareRESUMO
SUMMARY: The development of de novo DNA synthesis, polymerase chain reaction (PCR), DNA sequencing and molecular cloning gave researchers unprecedented control over DNA and DNA-mediated processes. To reduce the error probabilities of these techniques, DNA composition has to adhere to method-dependent restrictions. To comply with such restrictions, a synthetic DNA fragment is often adjusted manually or by using custom-made scripts. In this article, we present MESA (Mosla Error Simulator), a web application for the assessment of DNA fragments based on limitations of DNA synthesis, amplification, cloning, sequencing methods and biological restrictions of host organisms. Furthermore, MESA can be used to simulate errors during synthesis, PCR, storage and sequencing processes. AVAILABILITY AND IMPLEMENTATION: MESA is available at mesa.mosla.de, with the source code available at github.com/umr-ds/mesa_dna_sim. CONTACT: dominik.heider@uni-marburg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
DNA , Software , DNA/genética , Sequenciamento de Nucleotídeos em Larga Escala , Reação em Cadeia da Polimerase , Análise de Sequência de DNARESUMO
Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively.
Assuntos
Células Eucarióticas/citologia , Processamento de Imagem Assistida por Computador/métodos , Microscopia de Fluorescência/métodos , Redes Neurais de Computação , Biologia Computacional , Aprendizado Profundo , Humanos , Macrófagos/citologia , Células THP-1RESUMO
BACKGROUND: Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. RESULTS: We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub ( https://github.com/MW55/Natrix ) or as a Docker container on DockerHub ( https://hub.docker.com/r/mw55/natrix ). CONCLUSION: Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Software , Fluxo de Trabalho , Análise por Conglomerados , DNA Ambiental/genética , DNA Ambiental/isolamento & purificação , Análise de Dados , Bases de Dados Genéticas , Inundações , Microbiota/genética , Reprodutibilidade dos TestesRESUMO
Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.
RESUMO
Motivation: There has been rapid progress in the development of error-correcting and constrained codes for DNA storage systems in recent years. However, improving the steps for processing raw sequencing data for DNA storage has a lot of untapped potential for further progress. In particular, constraints can be used as prior information to improve the processing of DNA sequencing data. Furthermore, a workflow tailored to DNA storage codes enables fair comparisons between different approaches while leading to reproducible results. Results: We present RepairNatrix, a read-processing workflow for DNA storage. RepairNatrix supports preprocessing of raw sequencing data for DNA storage applications and can be used to flag and heuristically repair constraint-violating sequences to further increase the recoverability of encoded data in the presence of errors. Compared to a preprocessing strategy without repair functionality, RepairNatrix reduced the number of raw reads required for the successful, error-free decoding of the input files by a factor of 25-35 across different datasets. Availability and implementation: RepairNatrix is available on Github: https://github.com/umr-ds/repairnatrix.
RESUMO
The extensive information capacity of DNA, coupled with decreasing costs for DNA synthesis and sequencing, makes DNA an attractive alternative to traditional data storage. The processes of writing, storing, and reading DNA exhibit specific error profiles and constraints DNA sequences have to adhere to. We present DNA-Aeon, a concatenated coding scheme for DNA data storage. It supports the generation of variable-sized encoded sequences with a user-defined Guanine-Cytosine (GC) content, homopolymer length limitation, and the avoidance of undesired motifs. It further enables users to provide custom codebooks adhering to further constraints. DNA-Aeon can correct substitution errors, insertions, deletions, and the loss of whole DNA strands. Comparisons with other codes show better error-correction capabilities of DNA-Aeon at similar redundancy levels with decreased DNA synthesis costs. In-vitro tests indicate high reliability of DNA-Aeon even in the case of skewed sequencing read distributions and high read-dropout.
Assuntos
Replicação do DNA , DNA , Reprodutibilidade dos Testes , DNA/genética , Análise de Sequência de DNA , AlgoritmosRESUMO
In personalized medicine, a challenging task is to identify the most effective treatment for a patient. In oncology, several computational models have been developed to predict the response of drugs to therapy. However, the performance of these models depends on multiple factors. This paper presents a new approach, called Q-Rank, to predict the sensitivity of cell lines to anti-cancer drugs. Q-Rank integrates different prediction algorithms and identifies a suitable algorithm for a given application. Q-Rank is based on reinforcement learning methods to rank prediction algorithms on the basis of relevant features (e.g., omics characterization). The best-ranked algorithm is recommended and used to predict the response of drugs to therapy. Our experimental results indicate that Q-Rank outperforms the integrated models in predicting the sensitivity of cell lines to different drugs.
Assuntos
Antineoplásicos , Neoplasias , Preparações Farmacêuticas , Algoritmos , Antineoplásicos/uso terapêutico , Humanos , Neoplasias/tratamento farmacológico , Medicina de PrecisãoRESUMO
In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.
Assuntos
Sítios de Ligação , Biologia Computacional/métodos , Gráficos por Computador , Proteínas/química , Alinhamento de Sequência/métodos , Proteínas/genética , Análise de Sequência de Proteína/métodos , SoftwareRESUMO
Sleep medicine is gaining more and more interest and importance both within medical research and clinical routine. The investigation of sleep and associated disorders requires the overnight acquisition of a huge amount of biosignal data derived from various sensors (polysomnographic recording) as well as consecutive time-consuming manual analysis (polysomnographic analysis). Therefore, the development of automatic analysis systems has become a major focus in sleep research in the recent years, resulting in the development of algorithms for the analysis of different biosignals (EEG, ECG, EMG, breathing signals). In this study, an open source algorithm published by Hamilton et al. was used for ECG analysis, whereas the analysis of breathing signals was done using an algorithm published by Clark et al. using also variations of the intra-thoracic pressure for the detection of breathing disorders. The electromyogram (EMG) analysis was done with a self-made algorithm, whereas EEG analyses are currently under development, using both frequency analysis modules and pattern recognition procedures. Although all these algorithms have proved to be quite useful, their validity and reliability still needs to be verified in future studies. Taking into account that during a standard polysomnographic recording data from approximately 8 hours of sleep are collected, it is imaginable that processing this amount of data by the described algorithms very often exceeds the calculating capacity of current standard computers. Using Grid technology, this limitation can be transcended by splitting biosignal data and distributing it to several analysis computers. Therefore, Grid based automatic analysis systems may improve the effectiveness of polysomnographic investigations and thereby diminish the costs for health care providers.
Assuntos
Eletrocardiografia , Informática Médica , Polissonografia/métodos , Transtornos do Sono-Vigília/diagnóstico , Algoritmos , Humanos , SoftwareRESUMO
This paper presents a novel health analysis approach for heart failure prediction. It is based on the use of complex event processing (CEP) technology, combined with statistical approaches. A CEP engine processes incoming health data by executing threshold-based analysis rules. Instead of having to manually set up thresholds, our novel statistical algorithm automatically computes and updates thresholds according to recorded historical data. Experimental results demonstrate the merits of our approach in terms of speed, precision, and recall.
Assuntos
Algoritmos , Biologia Computacional/métodos , Insuficiência Cardíaca , Modelos Estatísticos , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/epidemiologia , Humanos , Aprendizado de Máquina , Valor Preditivo dos TestesRESUMO
In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.
Assuntos
Técnicas de Ablação/instrumentação , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/cirurgia , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/cirurgia , Agulhas , Ondas de Rádio , Algoritmos , Humanos , Recidiva , Tomografia Computadorizada por Raios XRESUMO
This paper introduces a neural network architecture for classifying feature vectors symbolizing portions (or segments) of an electroencephalogram (EEG) trace of a human subject. This classification task is the one that is typically required when developing a so-called brain-computer interface (BCI), which analyzes the EEG signals of a subject in order to "understand" the subject's thoughts. However, instead of merely saying which "category of thoughts" (i.e., which class) the respective input feature vector belongs to, the network described here estimates the probabilities of an EEG segment being associated with each individual class. The network, which is called PeGNC (for probability estimating guarded neural classifier), is tested with two kinds of experiments. In the first experiment, the alpha-rhythm associated with a human subject closing the eyes is detected online with the help of a frequency-based representation. Since the EEG signal is, in general, always a mixture of numerous action potentials generated simultaneously and it is, thus, very likely that mental activities result in overlapping classes, it is reasonable to believe that the PeGNC network--which does not select any one single class, but determines probability values for each mental category--is particularly suitable for this kind of EEG analysis. The second experiment deals with this issue on the basis of an offline analysis of simulated data.
Assuntos
Algoritmos , Inteligência Artificial , Encéfalo/fisiologia , Cognição/fisiologia , Eletroencefalografia/métodos , Redes Neurais de Computação , Interface Usuário-Computador , Adulto , Potenciais Evocados/fisiologia , Humanos , Masculino , Modelos Neurológicos , Modelos Estatísticos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Δ. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.
Assuntos
Anatomia Transversal/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Coluna Vertebral/anatomia & histologia , Algoritmos , Humanos , Tamanho do ÓrgãoRESUMO
In this contribution, a scale-invariant image segmentation algorithm is introduced that "wraps" the algorithm's parameters for the user by its interactive behavior, avoiding the definition of "arbitrary" numbers that the user cannot really understand. Therefore, we designed a specific graph-based segmentation method that only requires a single seed-point inside the target-structure from the user and is thus particularly suitable for immediate processing and interactive, real-time adjustments by the user. In addition, color or gray value information that is needed for the approach can be automatically extracted around the user-defined seed point. Furthermore, the graph is constructed in such a way, so that a polynomial-time mincut computation can provide the segmentation result within a second on an up-to-date computer. The algorithm presented here has been evaluated with fixed seed points on 2D and 3D medical image data, such as brain tumors, cerebral aneurysms and vertebral bodies. Direct comparison of the obtained automatic segmentation results with costlier, manual slice-by-slice segmentations performed by trained physicians, suggest a strong medical relevance of this interactive approach.
Assuntos
Pontos de Referência Anatômicos/patologia , Encefalopatias/patologia , Encéfalo/patologia , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Interface Usuário-Computador , Algoritmos , Sistemas Computacionais , Retroalimentação , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Pesquisa Translacional Biomédica/métodosRESUMO
Among all abnormal growths inside the skull, the percentage of tumors in sellar region is approximately 10-15%, and the pituitary adenoma is the most common sellar lesion. A time-consuming process that can be shortened by using adequate algorithms is the manual segmentation of pituitary adenomas. In this contribution, two methods for pituitary adenoma segmentation in the human brain are presented and compared using magnetic resonance imaging (MRI) patient data from the clinical routine: Method A is a graph-based method that sets up a directed and weighted graph and performs a min-cut for optimal segmentation results: Method B is a balloon inflation method that uses balloon inflation forces to detect the pituitary adenoma boundaries. The ground truth of the pituitary adenoma boundaries - for the evaluation of the methods - are manually extracted by neurosurgeons. Comparison is done using the Dice Similarity Coefficient (DSC), a measure for spatial overlap of different segmentation results. The average DSC for all data sets is 77.5±4.5% for the graph-based method and 75.9±7.2% for the balloon inflation method showing no significant difference. The overall segmentation time of the implemented approaches was less than 4s - compared with a manual segmentation that took, on the average, 3.9±0.5min.
Assuntos
Adenoma/patologia , Imageamento por Ressonância Magnética/métodos , Neoplasias Hipofisárias/patologia , Algoritmos , Gráficos por Computador , Simulação por Computador , Humanos , Imageamento por Ressonância Magnética/estatística & dados numéricos , Modelos AnatômicosRESUMO
Diffusion Tensor Imaging (DTI) and fiber tractography are established methods to reconstruct major white matter tracts in the human brain in-vivo. Particularly in the context of neurosurgical procedures, reliable information about the course of fiber bundles is important to minimize postoperative deficits while maximizing the tumor resection volume. Since routinely used deterministic streamline tractography approaches often underestimate the spatial extent of white matter tracts, a novel approach to improve fiber segmentation is presented here, considering clinical time constraints. Therefore, fiber tracking visualization is enhanced with statistical information from multiple tracking applications to determine uncertainty in reconstruction based on clinical DTI data. After initial deterministic fiber tracking and centerline calculation, new seed regions are generated along the result's midline. Tracking is applied to all new seed regions afterwards, varying in number and applied offset. The number of fibers passing each voxel is computed to model different levels of fiber bundle membership. Experimental results using an artificial data set of an anatomical software phantom are presented, using the Dice Similarity Coefficient (DSC) as a measure of segmentation quality. Different parameter combinations were classified to be superior to others providing significantly improved results with DSCs of 81.02%±4.12%, 81.32%±4.22% and 80.99%±3.81% for different levels of added noise in comparison to the deterministic fiber tracking procedure using the two-ROI approach with average DSCs of 65.08%±5.31%, 64.73%±6.02% and 65.91%±6.42%. Whole brain tractography based on the seed volume generated by the calculated seeds delivers average DSCs of 67.12%±0.86%, 75.10%±0.28% and 72.91%±0.15%, original whole brain tractography delivers DSCs of 67.16%, 75.03% and 75.54%, using initial ROIs as combined include regions, which is clearly improved by the repeated fiber tractography method.