Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2014 ◽  
Vol 7 (2) ◽  
pp. 75-87 ◽  
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

scholarly journals Three-dimensional semi-automated volumetric assessment of the pulp space of teeth following regenerative dental procedures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heeresh Shetty ◽  
Shishir Shetty ◽  
Adesh Kakade ◽  
Aditya Shetty ◽  
Mohmed Isaqali Karobari ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
Three Dimensional ◽  
Volume Estimation ◽  
Permanent Teeth ◽  
3D Slicer ◽  
Link Type ◽  
Volumetric Assessment ◽  
Critical Measure ◽  
Significant Difference

AbstractThe volumetric change that occurs in the pulp space over time represents a critical measure when it comes to determining the secondary outcomes of regenerative endodontic procedures (REPs). However, to date, only a few studies have investigated the accuracy of the available domain-specialized medical imaging tools with regard to three-dimensional (3D) volumetric assessment. This study sought to compare the accuracy of two different artificial intelligence-based medical imaging programs namely OsiriX MD (v 9.0, Pixmeo SARL, Bernex Switzerland, https://www.osirix-viewer.com) and 3D Slicer (http://www.slicer.org), in terms of estimating the volume of the pulp space following a REP. An Invitro assessment was performed to check the reliability and sensitivity of the two medical imaging programs in use. For the subsequent clinical application, pre- and post-procedure cone beam computed tomography scans of 35 immature permanent teeth with necrotic pulp and periradicular pathosis that had been treated with a cell-homing concept-based REP were processed using the two biomedical DICOM software programs (OsiriX MD and 3D Slicer). The volumetric changes in the teeth’s pulp spaces were assessed using semi-automated techniques in both programs. The data were statistically analyzed using t-tests and paired t-tests (P = 0.05). The pulp space volumes measured using both programs revealed a statistically significant decrease in the pulp space volume following the REP (P < 0.05), with no significant difference being found between the two programs (P > 0.05). The mean decreases in the pulp space volumes measured using OsiriX MD and 3D Slicer were 25.06% ± 19.45% and 26.10% ± 18.90%, respectively. The open-source software (3D Slicer) was found to be as accurate as the commercially available software with regard to the volumetric assessment of the post-REP pulp space. This study was the first to demonstrate the step-by-step application of 3D Slicer, a user-friendly and easily accessible open-source multiplatform software program for the segmentation and volume estimation of the pulp spaces of teeth treated with REPs.

A LARGE NEURINOMA ORIGINATING FROM THE ANTERIOR INTEROSSEOUS NERVE

Hand Surgery ◽  
2012 ◽  
Vol 17 (03) ◽  
pp. 375-377
Author(s):  
Tomoo Inukai ◽  
Kenzo Uchida ◽  
Hisatoshi Baba
Keyword(s):  
Magnetic Resonance ◽  
Surgical Planning ◽  
Three Dimensional ◽  
Interesting Case ◽  
3D Ct ◽  
Mr Images ◽  
Anterior Interosseous Artery ◽  
Anterior Interosseous Nerve ◽  
3D Ct Angiography ◽  
Interosseous Artery

We report an interesting case of a neurinoma originating from the anterior interosseous nerve. Magnetic resonance (MR) images showed an egg-shaped, well-circumscribed mass on the volar side of the forearm. On the enhanced three-dimensional computer tomography (3D-CT), it was clearly demonstrated that the tumour had arterial feeding from the anterior interosseous artery. The enhanced 3D-CT angiography was useful in the pre-operative diagnosis and surgical planning of peripheral neurinomas.

scholarly journals A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Min Xu ◽  
Pengjiang Qian ◽  
Jiamin Zheng ◽  
Hongwei Ge ◽  
Raymond F. Muzic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Magnetic Resonance ◽  
Large Scale ◽  
Medical Images ◽  
High Tech ◽  
Mr Images ◽  
Specific Target ◽  
Radial Basis ◽  
Artificial Neural

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

scholarly journals Magnetic resonance image evaluation of pallidotomy lesions: a volumetric and shape analysis

1997 ◽  
Vol 2 (3) ◽  
pp. E5 ◽  
Author(s):  
Jeffrey M. Burns ◽  
Steve Wilkinson ◽  
John Overman ◽  
Jennifer Kieltyka ◽  
Thorsten Lundsgaarde ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Globus Pallidus ◽  
Magnetic Resonance Image ◽  
Three Dimensional ◽  
Volumetric Analysis ◽  
Lesion Volume ◽  
Slice Thickness ◽  
Mr Images ◽  
Ellipsoid Volume ◽  
Volume Estimates

Determination of acute pallidotomy-produced lesion volumes, pre- and postpallidotomy globus pallidus (GP) volumes, and assessment of lesion shape using magnetic resonance (MR) imaging-based computerized segmentation (contouring) and three-dimensional rendering was made in 19 patients. Magnetic resonance image slice thickness (1.5 mm or 6 mm) was not found to be a significant factor influencing contour-based pallidotomy lesion volume estimates. Previously reported lesion volumes produced by pallidotomy have often been estimated using the ellipsoid volume formula. Using 1.5-mm-thick MR sections, contour-based pallidotomy-produced lesion volumes were significantly different from those volumes estimated by the ellipsoid formula. Globus pallidus volumes, estimated by contouring T2-weighted MR images, were bilaterally similar (2.4 ± 0.37 ml [right]; 2.2 ± 0.45 ml [left]). Postoperative GP volumes were found on the contralateral, unlesioned side to be 2 ± 0.45 ml and on the lesioned side to be 1.25 ± 0.45 ml. Using the contralateral, unlesioned side as a reference volume, approximately 39 ± 14% of the GP was visibly affected on the lesioned side. Seventeen of 18 patients had a favorable outcome with reduced dyskinesias and "off" time with improvement in parkinsonian symptoms. Analysis of computerized three-dimensional rendering of pallidotomy-produced lesions based on MR images showed no relationship between lesioning technique and resulting lesion shape. Important factors in the volumetric analysis of pallidotomy lesions are identified and allow reasonable assessment of the pallidotomy lesion volume and shape and the extent of the affected GP.

scholarly journals SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

2020 ◽  
pp. 299-309 ◽  
Author(s):  
Fan Zhang ◽  
Thomas Noh ◽  
Parikshit Juvekar ◽  
Sarah F. Frisken ◽  
Laura Rigolo ◽  
...  
Keyword(s):  
Open Source ◽  
Diffusion Tensor ◽  
Intraoperative Imaging ◽  
Time Integration ◽  
Three Dimensional ◽  
High Grade Glioma ◽  
Patient Specific ◽  
Imaging Data ◽  
3D Slicer ◽  
Research Software

PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas—magnetic resonance imaging coregistration method

2003 ◽  
Vol 99 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Jérôme Yelnik ◽  
Philippe Damier ◽  
Sophie Demeret ◽  
David Gervais ◽  
Eric Bardinet ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Mr Imaging ◽  
Parkinson Disease ◽  
Three Dimensional ◽  
Zona Incerta ◽  
Mr Images ◽  
Content Type ◽  
Anatomical Localization ◽  
Stimulating Electrodes ◽  
Fine Print

Object. The aim of this study was to correlate the clinical improvement in patients with Parkinson disease (PD) treated using deep brain stimulation (DBS) of the subthalamic nucleus (STN) with the precise anatomical localization of stimulating electrodes. Methods. Localization was determined by superimposing figures from an anatomical atlas with postoperative magnetic resonance (MR) images obtained in each patient. This approach was validated by an analysis of experimental and clinical MR images of the electrode, and the development of a three-dimensional (3D) atlas—MR imaging coregistration method. The PD motor score was assessed through two contacts for each of two electrodes implanted in 10 patients: the “therapeutic contact” and the “distant contact” (that is, the next but one to the therapeutic contact). Seventeen therapeutic contacts were located within or on the border of the STN, most of which were associated with significant improvement of the four PD symptoms tested. Therapeutic contacts located in other structures (zona incerta, lenticular fasciculus, or midbrain reticular formation) were also linked to a significant positive effect. Stimulation applied through distant contacts located in the STN improved symptoms of PD, whereas that delivered through distant contacts in the remaining structures had variable effects ranging from worsening of symptoms to their improvement. Conclusions. The authors have demonstrated that 3D atlas—MR imaging coregistration is a reliable method for the precise localization of DBS electrodes on postoperative MR images. In addition, they have confirmed that although the STN is the main target during DBS treatment for PD, stimulation of surrounding regions, particularly the zona incerta or the lenticular fasciculus, can also improve symptoms of PD.

scholarly journals Medical Images Segmentation Based on Unsupervised Algorithms: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Revella E. A. Armya Armya ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Automatic Process ◽  
X Rays ◽  
Resonance Imaging ◽  
Unsupervised Algorithms

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

scholarly journals Tetrahedral mesh generation for medical imaging

2005 ◽  
Author(s):  
Andriy Fedorov ◽  
Nikos Chrisochoides ◽  
Ron Kikinis ◽  
Simon Warfield
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
Mesh Generation ◽  
Tetrahedral Mesh ◽  
Deformable Objects ◽  
Mr Images ◽  
Mesh Generator ◽  
Preliminary Results ◽  
Tetrahedral Mesh Generation

We describe the open source implementation of an adaptive tetrahedral mesh generator particularly targeted for non-rigid FEM registration of MR images. While many medical imaging applications require robust mesh generation, there are few codes available. Moreover, most of the practical implementations are commercial. The algorithm we have implemented has been previously evaluated for simulations of highly deformable objects, and the preliminary results show its applicability to the targeted application. The implementation we describe is open source and will be available within Insight Toolkit.

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