Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

Momi, Elena De; Caborni, Chiara; Cardinale, Francesco; Casaceli, Giuseppe; Castana, Laura; Cossu, Massimo; Mai, Roberto; Gozzo, Francesca; Francione, Stefano; Tassi, Laura; Russo, Giorgio Lo; Antiga, Luca; Ferrigno, Giancarlo

doi:10.1007/s11548-014-1004-1

Cited by 60 publications

(63 citation statements)

References 33 publications

(40 reference statements)

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“…Blood vessel analysis plays a fundamental role in different clinical fields, such as laryngology, oncology [1], ophthalmology [2], and neurosurgery [3]- [5] [6], both for diagnosis, treatment planning and execution, and for treatment outcome evaluation and follow up.…”

Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

View full text Add to dashboard Cite

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“…The Robocast project ended in 2011 and has been continued by the Active project -acronym for Active Constraints Technologies for Ill-defined or Volatile Environments (FP7-ICT-2009-6-270460) [61], [62], which proposes an integrated redundant robotic platform. This relies on two autonomous cooperating robotic manipulators for neurosurgery, which form a light and agile system with 20 DoF.…”

Section: ) Robocastmentioning

confidence: 99%

Review of Robotic Technology for Stereotactic Neurosurgery

Faria

Erlhagen

Rito

et al. 2015

IEEE Rev. Biomed. Eng.

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The research of stereotactic apparatus to guide surgical devices began in 1908, yet a major part of today's stereotactic neurosurgeries still rely on stereotactic frames developed almost half a century ago. Robots excel at handling spatial information, and are, thus, obvious candidates in the guidance of instrumentation along precisely planned trajectories. In this review, we introduce the concept of stereotaxy and describe a standard stereotactic neurosurgery. Neurosurgeons' expectations and demands regarding the role of robots as assistive tools are also addressed. We list the most successful robotic systems developed specifically for or capable of executing stereotactic neurosurgery. A critical review is presented for each robotic system, emphasizing the differences between them and detailing positive features and drawbacks. An analysis of the listed robotic system features is also undertaken, in the context of robotic application in stereotactic neurosurgery. Finally, we discuss the current perspective, and future directions of a robotic technology in this field. All robotic systems follow a very similar and structured workflow despite the technical differences that set them apart. No system unequivocally stands out as an absolute best. The trend of technological progress is pointing toward the development of miniaturized cost-effective solutions with more intuitive interfaces.

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“…As needed, other image datasets, such as fluidattenuated inversion recovery MR scans (FLAIR), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI) or positron emission tomography (PET), were included in the multimodal scene. This image processing workflow is routinely performed at the "Claudio Munari" Centre [22][23][24][25][26]. …”

Section: Preoperative Data Acquisition and Processingmentioning

confidence: 99%

A method for the assessment of time-varying brain shift during navigated epilepsy surgery

et al. 2015

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Purpose Image guidance is widely used in neurosurgery. Tracking systems (neuronavigators) allow registering the preoperative image space to the surgical space. The localization accuracy is influenced by technical and clinical factors, such as brain shift. This paper aims at providing quantitative measure of the time-varying brain shift during open epilepsy surgery, and at measuring the pattern of brain deformation with respect to three potentially meaningful parameters: craniotomy area, craniotomy orientation and gravity vector direction in the images reference frame. Methods We integrated an image-guided surgery system with 3D Slicer, an open-source package freely available in the Internet. We identified the preoperative position of several cortical features in the image space of 12 patients, inspecting both the multiplanar and the 3D reconstructions. We subsequently repeatedly tracked their position in the surgical space. Therefore, we measured the cortical shift, following its time-related changes and estimating its correlation with gravity and craniotomy normal directions. Results The mean of the median brain shift amount is 9.64 mm (SD = 4.34 mm). The brain shift amount resulted not correlated with respect to the gravity direction, the craniotomy normal, the angle between the gravity and the craniotomy normal and the craniotomy area. Conclusions Our method, which relies on cortex surface 3D measurements, gave results, which are consistent with liter-

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Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

Cited by 60 publications

References 33 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Review of Robotic Technology for Stereotactic Neurosurgery

A method for the assessment of time-varying brain shift during navigated epilepsy surgery

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