References of "Baniasadi, Mehri 50019745"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailDBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Baniasadi, Mehri UL; Petersen, Mikkel V.; Goncalves, Jorge UL et al

in Human Brain Mapping (2022)

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by ... [more ▼]

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage. [less ▲]

Detailed reference viewed: 14 (1 UL)
See detailComputational Tools for Evaluation and Programing of Deep Brain Stimulation
Baniasadi, Mehri UL

Doctoral thesis (2022)

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of numerous movement and psychiatric disorders by electrical stimulation of specific neural tissues via implanted electrodes ... [more ▼]

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of numerous movement and psychiatric disorders by electrical stimulation of specific neural tissues via implanted electrodes. Precise electrode implantation is important to target the right brain area. After the surgery, DBS parameters, including stimulation amplitude, frequency, pulse width, and selection of electrode’s active contacts, are adjusted during programming sessions. Programming sessions are normally done by trial and error. Thus, they can be long and tiring. The main goal of the thesis is to make the post-operative experience, particularly the programming session, easier and faster by using visual aids to create a virtual reconstruction of the patient’s case. This enables in silico testing of different scenarios before applying them to the patient. A quick and easy-to-use deep learning-based tool for deep brain structure segmentation is developed with 89% ± 3 accuracy (DBSegment). It is much easier to implement compared to widely-used registration-based methods, as it requires less dependencies and no parameter tuning. Therefore, it is much more practical. Moreover, it segments 40 times faster than the registration-based method. This method is combined with an electrode localization method to reconstruct patients’ cases. Additionally, we developed a tool that simulates DBS-induced electric field distributions in less than a seconds (FastField). This is 1000 times faster than standard methods based on finite elements, with nearly the same performance (92%). The speed of the electric field simulation is particularly important for DBS parameter initialization, which we initialize by solving an optimization problem (OptimDBS). A grid search method confirms that our novel approach convergences to the global minimum. Finally, all the developed methods are tested on clinical data to ensure their applicability. In conclusion, this thesis develops various novel user-friendly tools, enabling efficient and accurate DBS reconstruction and parameter initialization. The methods are by far the quickest among open-source tools. They are easy to use and publicly available, FastField within the LeadDBS toolbox, and DBSegment as a Python pip package and a Docker image. We hope they can boost the DBS post-operative experience, maximize the therapy’s efficacy, and ameliorate DBS research. [less ▲]

Detailed reference viewed: 77 (5 UL)
Peer Reviewed
See detailInitialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data
Baniasadi, Mehri UL; Husch, Andreas UL; Proverbio, Daniele UL et al

in Bildverarbeitung für die Medizin 2022 (2022)

Following the deep brain stimulation (DBS) surgery, the stimulation parameters are manually tuned to reduce symptoms. This procedure can be timeconsuming, especially with directional leads. We propose an ... [more ▼]

Following the deep brain stimulation (DBS) surgery, the stimulation parameters are manually tuned to reduce symptoms. This procedure can be timeconsuming, especially with directional leads. We propose an automated methodology to initialise contact configurations using imaging techniques. The goal is to maximise the electric field on the target while minimising the spillover, and the electric field on regions of avoidance. By superposing pre-computed electric fields, we solve the optimisation problem in less than a minute, much more efficient compared to finite element methods. Our method offers a robust and rapid solution, and it is expected to considerably reduce the time required for manual parameter tuning. [less ▲]

Detailed reference viewed: 69 (8 UL)
Full Text
Peer Reviewed
See detailFastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields
Baniasadi, Mehri UL; Proverbio, Daniele UL; Goncalves, Jorge UL et al

in NeuroImage (2020)

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes ... [more ▼]

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method–FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable e-field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2s, ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications [less ▲]

Detailed reference viewed: 200 (32 UL)
Full Text
Peer Reviewed
See detailImpressive weight gain after deep brain stimulation of nucleus accumbens in treatment- ­ resistant bulimic anorexia nervosa
Arroteia, Isabel Fernandes; Husch, Andreas UL; Baniasadi, Mehri UL et al

in BMJ Case Reports (2020)

Anorexia nervosa (AN) severely impacts individual’s mental and physical health as well as quality of life. In 21% of cases no durable response to conservative treatment can be obtained. The serious course ... [more ▼]

Anorexia nervosa (AN) severely impacts individual’s mental and physical health as well as quality of life. In 21% of cases no durable response to conservative treatment can be obtained. The serious course of the disease in the most severely affected patients justifies invasive treatment options. One of the treatment methods increasingly used in recent years is deep brain stimulation (DBS). A 42-year- old woman suffering from chronic AN of the bulimic subtype shows a 46.9% weight gain and a subjective increase in quality of life, 12 months after bilateral nucleus accumbens (NAcc) DBS implantation. No improvement in comorbid depression could be achieved. DBS of the NAcc is a treatment option to be considered in severe AN when conventional treatment modalities recommended by evidence-based guidelines have not been able to bring lasting relief to the patient’s suffering. [less ▲]

Detailed reference viewed: 104 (12 UL)