References of "Hertel, Frank 50009071"
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See detailModel bias and its impact on computer-aided diagnosis: A data-centric approach
Garcia Santa Cruz, Beatriz UL; Bossa, Matias Nicolas UL; Sölter, Jan UL et al

Poster (2021, August)

Machine learning and data-driven solutions open exciting opportunities in many disciplines including healthcare. The recent transition to this technology into real clinical settings brings new challenges ... [more ▼]

Machine learning and data-driven solutions open exciting opportunities in many disciplines including healthcare. The recent transition to this technology into real clinical settings brings new challenges. Such problems derive from several factors, including their dataset origin, composition and description, hampering their fairness and secure application. Considering the potential impact of incorrect predictions in applied-ML healthcare research is urgent. Undetected bias induced by inappropriate use of datasets and improper consideration of confounders prevents the translation of prediction models into clinical practice. Therefore, in this work, the use of available systematic tools to assess the risk of bias in models is employed as the first step to explore robust solutions for better dataset choice, dataset merge and design of the training and validation step during the ML development pipeline. [less ▲]

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See detailApplication of Raman Spectroscopy for Detection of Histologically Distinct Areas in Formalin-fixed Paraffin-embedded (FFPE) Glioblastoma
Klamminger, Gilbert Georg; Gerardy, Jean-Jacques UL; Jelke, Finn et al

in Neuro-Oncology Advances (2021)

Background Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably ... [more ▼]

Background Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. Methods To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up a SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. Results Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal appearing brain tissue can be detected. Conclusion These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As a conclusion, we propose that RS may serve useful as a future method in the pathological toolbox. [less ▲]

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See detailAutomated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images.
Vlasov, Vanja UL; Bofferding, Marie UL; Marx, Loic Marc UL et al

in Bildverarbeitung für die Medizin 2021 (2021)

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See detailA rule-based expert system for real-time feedback-control in deep brain stimulation
Bremm, René Peter UL; Koch, Klaus Peter; Krüger, Rejko UL et al

in Current Directions in Biomedical Engineering (2020), 6(3), 4

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See detailAnalysis and visualisation of tremor dynamics in deep brain stimulation patients
Bremm, René Peter UL; Koch, Klaus Peter; Krüger, Rejko UL et al

in Current Directions in Biomedical Engineering (2020), 6(3), 4

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See detailPatient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology.
Golebiewska, Anna UL; Hau, Ann-Christin; Oudin, Anaïs et al

in Acta Neuropathologica (2020)

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique ... [more ▼]

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique organoids and patient-derived orthotopic xenografts (PDOX) of various glioma subtypes, including gliomas with mutations in IDH1, and paired longitudinal PDOX from primary and recurrent tumors of the same patient. We show that glioma PDOXs enable long-term propagation of patient tumors and represent clinically relevant patient avatars that retain histopathological, genetic, epigenetic, and transcriptomic features of parental tumors. We find no evidence of mouse-specific clonal evolution in glioma PDOXs. Our cohort captures individual molecular genotypes for precision medicine including mutations in IDH1, ATRX, TP53, MDM2/4, amplification of EGFR, PDGFRA, MET, CDK4/6, MDM2/4, and deletion of CDKN2A/B, PTCH, and PTEN. Matched longitudinal PDOX recapitulate the limited genetic evolution of gliomas observed in patients following treatment. At the histological level, we observe increased vascularization in the rat host as compared to mice. PDOX-derived standardized glioma organoids are amenable to high-throughput drug screens that can be validated in mice. We show clinically relevant responses to temozolomide (TMZ) and to targeted treatments, such as EGFR and CDK4/6 inhibitors in (epi)genetically defined subgroups, according to MGMT promoter and EGFR/CDK status, respectively. Dianhydrogalactitol (VAL-083), a promising bifunctional alkylating agent in the current clinical trial, displayed high therapeutic efficacy, and was able to overcome TMZ resistance in glioblastoma. Our work underscores the clinical relevance of glioma organoids and PDOX models for translational research and personalized treatment studies and represents a unique publicly available resource for precision oncology. [less ▲]

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See detailAutomatic Detection of Nigrosome Degeneration in Susceptibility-Weighted MRI for Computer-Aided Diagnosis of Parkinson’s Disease Using Machine Learning
Garcia Santa Cruz, Beatriz UL; Husch, Andreas UL; Hertel, Frank UL

in Movement Disorders: Volume 35, Number S1, September 2020 (2020, September 12)

Objective: Automatize the detection of ‘swallow-tail’ appearance in substantia nigra dopaminergic neurons using MRI for more robust tests on Parkinson’s disease (PD) diagnosis. Background: Differential ... [more ▼]

Objective: Automatize the detection of ‘swallow-tail’ appearance in substantia nigra dopaminergic neurons using MRI for more robust tests on Parkinson’s disease (PD) diagnosis. Background: Differential diagnosis of PD is challenging even in specialized centers. The use of imaging techniques can be bene cial for the diagnosis. Although DaTSCAN has been proven to be clinically useful, it is not widely available and has radiation risk and high-cost associated. Therefore, MRI scans for PD diagnosis offer several advantages over DaTSCAN [1]. Recent literature shows strong evidence of high diagnostic accuracy using the ‘swallow-tail’ shape of the dorsolateral substantia nigra in 3T – SWI [2]. Nevertheless, the majority of such studies rely on the subjective opinion of experts and manual methods for the analysis to assess the accuracy of these features. Alternatively, we propose a fully automated solution to evaluate the absence or presence of this feature for computer-aided diagnosis (CAD) of PD. Method: Restrospective study of 27 PD and 18 non-PD was conducted, including standard high-resolution 3D MRI – T1 & SWI sequences (additionally, T2 scans were used to increase the registration references). Firstly, spatial registration and normalization of the images were performed. Then, the ROI was extracted using atlas references. Finally, a supervised machine learning model was built using 5-fold-within-5-fold nested cross-validation. Results: Preliminary results show signi cant sensitivity (0.92) and ROC AUC (0.82), allowing for automated classi cation of patients based on swallow-tail biomarker from MRI. Conclusion: Detection of nigrosome degeneration (swallow-tail biomarker) in accessible brain imaging techniques can be automatized with signi cant accuracy, allowing for computer-aided PD diagnosis. References: [1] Schwarz, S. T., Xing, Y., Naidu, S., Birchall, J., Skelly, R., Perkins, A., ... & Gowland, P. (2017). Protocol of a single group prospective observational study on the diagnostic value of 3T susceptibility weighted MRI of nigrosome-1 in patients with parkinsonian symptoms: the N3iPD study (nigrosomal iron imaging in Parkinson’s disease). BMJ open, 7(12), e016904. [2] – Schwarz, S. T., Afzal, M., Morgan, P. S., Bajaj, N., Gowland, P. A., & Auer, D. P. (2014). The ‘swallow tail’ appearance of the healthy nigrosome –a new accurate test of Parkinson’s disease: a case-control and retrospective cross-sectional MRI study at 3T. PloS one, 9(4). [less ▲]

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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 ▲]

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See detailPrimary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine
Golebiewska, Anna UL; Hau, Ann-Christin; Oudin, Anais et al

E-print/Working paper (2020)

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term ... [more ▼]

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies. [less ▲]

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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 ▲]

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See detailFrom tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Saraiva, Claudia UL et al

Poster (2019, November 29)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios. [less ▲]

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See detailDeep Learning Quality Control for High-Throughput High-Content Screening Microscopy Images
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Schwamborn, Jens Christian UL et al

Poster (2019, October 10)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput high-content screening (HTHCS) in which the quality of the results relays on the accuracy of image analysis. Deep learning (DL) yields great performance in image analysis tasks especially with big amounts of data such as the produced in HTHCS contexts. Such DL and HTHCS strength is also their biggest weakness since DL solutions are highly sensitive to bad quality datasets. Hence, accurate Quality Control (QC) for microscopy HTHCS becomes an essential step to obtain reliable pipelines for HTHCS analysis. Usually, artifacts found on these platforms are the consequence of out-of-focus and undesirable density variations. The importance of accurate outlier detection becomes essential for both the training process of generic ML solutions (i.e. segmentation or classification) and the QC of the input data such solution will predict on. Moreover, during the QC of the input dataset, we aim not only to discard unsuitable images but to report the user on the quality of its dataset giving the user the choice to keep or discard the bad images. To build the QC solution we employed fluorescent microscopy images of rosella biosensor generated in the HTHCS platform. A total of 15 planes ranging from -6z to +7z steps to the two optimum planes. We evaluated 27 known focus measure operators and concluded that they have low sensitivity in noisy conditions. We propose a CNN solution which predicts the focus error based on the distance to the optimal plane, outperforming the evaluated focus operators. This QC allows for better results in cell segmentation models based on U-Net architecture as well as promising improvements in image classification tasks. [less ▲]

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See detailPATH-29. POTENTIAL OF RAMAN SPECTROSCOPY IN ONCOLOGICAL NEUROSURGERY
Kleine Borgmann, Felix; Husch, Andreas UL; Slimani, Redouane et al

Poster (2019)

Raman spectroscopy (RS) has gained increasing interest for the analysis of biological tissues within the recent years. It is a label-free, non-destructive method providing insights in biochemical ... [more ▼]

Raman spectroscopy (RS) has gained increasing interest for the analysis of biological tissues within the recent years. It is a label-free, non-destructive method providing insights in biochemical properties of tumor cells. It is possible to compare RS signals with histological properties of identical tissue parts. Therefore, RS bears promising potentials in neurosurgical neurooncology. On one hand, it could potentially be used for both intraoperative tumor diagnostics and resection control. On the other hand, it could provide important knowledge on tumor biochemistry and used for a subclassification of tumors with a potential impact on personalized therapy approaches. Within our group, we analyzed over 3000 measurement points in different brain tumors ex vivo with a robotized RS system and correlated the spectral curves with histopathological results. We separated and subclassified the data by AI-based methods. Additionally, we compared the latter results with those of a handheld probe, which is potentially navigatable for in vivo, intraoperative applications. We could demonstrate, that it is possible to separate distinct tumor groups only based on RS signals, especially by using computer-based signal analysis. Furthermore, we could demonstrate the differences of the spectra of deep-frozen and formalin-fixed tissues versus non-fixed tissues. Based on our results, we will highlight the potentials of RS for intraoperative neurosurgical application in resection control for brain tumors, as well as we will focus on the potentials for brain tumor diagnostics based purely on this method or by using it as an adjunct. Those methods bear additional potentials in the field of personalized chemotherapy approaches. [less ▲]

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See detailHabenula deep brain stimulation for refractory bipolar disorder.
Zhang, Chencheng; Kim, Seung-Goo; Li, Dianyou et al

in Brain stimulation (2019)

Bipolar disorder (BD) is a mood disorder associated with significant morbidity and mortality. In many cases, BD can be managed with pharmacotherapy, psychological therapy, or electroconvulsive therapy [1 ... [more ▼]

Bipolar disorder (BD) is a mood disorder associated with significant morbidity and mortality. In many cases, BD can be managed with pharmacotherapy, psychological therapy, or electroconvulsive therapy [1]. For some afflicted patients, however, BD is a chronic and severely disabling condition that is resistant to the aforementioned treatments. Deep brain stimulation (DBS) offers a safe and effective neurosurgical treatment for otherwise refractory movement disorders and obsessive-compulsive disorder [2,3]. [less ▲]

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See detailDystone Bewegungsstörungen bei Kindern
Hertel, Frank UL

in Bächli, Heidi; Lütschg, Jürg; Messing-Jünger, Martina (Eds.) Pädiatrische Neurochirurgie (2018)

Bei den Dystonien handelt es sich um eine heterogene Gruppe verschiedenartiger Erkrankungen mit ähnlichen Symptomen. Klassifikation, Differenzialdiagnosen und Therapien sind komplex und sollten von ... [more ▼]

Bei den Dystonien handelt es sich um eine heterogene Gruppe verschiedenartiger Erkrankungen mit ähnlichen Symptomen. Klassifikation, Differenzialdiagnosen und Therapien sind komplex und sollten von spezialisierten Zentren durchgeführt werden. Die Erkrankungen können vom Säuglings- bis ins späte Erwachsenenalter auftreten. Das Spektrum reicht von oligosymptomatischen Formen bis hin zu solchen, die schwerste Beeinträchtigungen in der Lebensqualität und Behinderungen bedingen können. Zur konservativen Therapie werden Krankengymnastik, orale Medikamente sowie lokale Injektionsbehandlungen eingesetzt. Von neurochirurgisch operativer Seite existieren die Neuromodulationstherapien (implantierbare Pumpen, Tiefe Hirnstimulation) sowie ablative Verfahren. Die neuere Literatur zeigt, dass insbesondere die kognitive Entwicklung bei Kindern umso günstiger beeinflusst werden kann, je früher eine effektive Therapie erfolgt. [less ▲]

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See detailPaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation
Husch, Andreas UL; Petersen, Mikkel V.; Gemmar, Peter et al

in NeuroImage: Clinical (2018), 17

Abstract Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative ... [more ▼]

Abstract Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative reconstruction of the DBS electrodes is important for an efficient stimulation parameter tuning. A major limitation of existing approaches for electrode reconstruction from post-operative imaging that prevents the clinical routine use is that they are manual or semi-automatic, and thus both time-consuming and subjective. Moreover, the existing methods rely on a simplified model of a straight line electrode trajectory, rather than the more realistic curved trajectory. The main contribution of this paper is that for the first time we present a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories. The robustness of our proposed method is demonstrated using a multi-center clinical dataset consisting of N=44 electrodes. In all cases the electrode trajectories were successfully identified and reconstructed. In addition, the accuracy is demonstrated quantitatively using a high-accuracy phantom with known ground truth. In the phantom experiment, the method could detect individual electrode contacts with high accuracy and the trajectory reconstruction reached an error level below 100 μm (0.046 ± 0.025 mm). An implementation of the method is made publicly available such that it can directly be used by researchers or clinicians. This constitutes an important step towards future integration of lead reconstruction into standard clinical care. [less ▲]

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See detailPost-operative deep brain stimulation assessment: Automatic data integration and report generation
Husch, Andreas UL; Petersen, Mikkel V.; Gemmar, Peter et al

in Brain Stimulation (2018)

Background The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more ... [more ▼]

Background The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads. Objective Providing a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data. Methods We present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas. Results The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes. Discussion Automatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures. [less ▲]

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See detailShape-aware surface reconstruction from sparse 3D point-clouds
Bernard, Florian UL; Salamanca Mino, Luis UL; Thunberg, Johan UL et al

in Medical Image Analysis (2017), 38

The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process ... [more ▼]

The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data. [less ▲]

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See detailIntegration of sparse electrophysiological measurements with preoperative MRI using 3D surface estimation in deep brain stimulation surgery
Husch, Andreas UL; Gemmar, Peter; Thunberg, Johan UL et al

in Webster, Robert; Fei, Baowei (Eds.) Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling (2017, February 14)

Intraoperative microelectrode recordings (MER) have been used for several decades to guide neurosurgeons during the implantation of Deep Brain Stimulation (DBS) electrodes, especially when targeting the ... [more ▼]

Intraoperative microelectrode recordings (MER) have been used for several decades to guide neurosurgeons during the implantation of Deep Brain Stimulation (DBS) electrodes, especially when targeting the subthalamic nucleus (STN) to suppress the symptoms of Parkinson’s Disease. The standard approach is to use an array of up to five MER electrodes in a fixed configuration. Interpretation of the recorded signals yields a spatiallyvery sparse set of information about the morphology of the respective brain structures in the targeted area. However, no aid is currently available for surgeons to intraoperatively integrate this information with other data available on the patient’s individual morphology (e.g. MR imaging data used for surgical planning). This integration might allow surgeons to better determine the most probable position of the electrodes within the target structure during surgery. This paper suggests a method for reconstructing a surface patch from the sparse MER dataset utilizing additional a-priori knowledge about the geometrical configuration of the measurement electrodes. The conventional representation of MER measurements as intervals of target region/non-target region is therefore transformed into an equivalent boundary set representation, allowing efficient point-based calculations. Subsequently, the problem is to integrate the resulting patch with a preoperative model of the target structure, which can be formulated as registration problem minimizing a distance measure between the two surfaces. When restricting this registration procedure to translations, which is reasonable given certain geometric considerations, the problem can be solved globally by employing an exhaustive search with arbitrary precision in polynomial time. The proposed method is demonstrated using bilateral STN/Substantia Nigra segmentation data from preoperative MRIs of 17 Patients with simulated MER electrode placement. When using simulated data of heavily perturbed electrodes and subsequent MER measuremen [less ▲]

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See detailFast Correspondences for Statistical Shape Models of Brain Structures
Bernard, Florian UL; Vlassis, Nikos UL; Gemmar, Peter et al

in SPIE Medical Imaging (2016, March)

Detailed reference viewed: 228 (18 UL)