Visualisation of multimodal images for neurosurgical planning and guidance

2005 ◽  
pp. 40-46 ◽  
Author(s):  
J. Zhao ◽  
A. C. F. Colchester ◽  
C. J. Henri ◽  
D. Hawkes ◽  
C. Ruff
Keyword(s):  
Neurosurgical Planning ◽  
Multimodal Images

scholarly journals Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions

2021 ◽  
Vol 13 (7) ◽  
pp. 1380
Author(s):  
Sébastien Dandrifosse ◽  
Alexis Carlier ◽  
Benjamin Dumont ◽  
Benoît Mercatoris
Keyword(s):  
Accurate Method ◽  
Field Conditions ◽  
Plant Phenotyping ◽  
Average Error ◽  
Multiple Sources ◽  
Registration Method ◽  
Pixel Intensity ◽  
Local Distortion ◽  
Close Range ◽  
Multimodal Images

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.

scholarly journals LANGUAGE REORGANIZATION IN APHASICS

Neurosurgery ◽  
2008 ◽  
Vol 63 (3) ◽  
pp. 487-497 ◽  
Author(s):  
Timothy H. Lucas ◽  
Daniel L. Drane ◽  
Carl B. Dodrill ◽  
George A. Ojemann
Keyword(s):  
Brain Injury ◽  
Temporal Lobe ◽  
Intractable Epilepsy ◽  
Cortical Lesions ◽  
Language Mapping ◽  
Neurosurgical Planning ◽  
Anatomic Distribution ◽  
And Performance ◽  
And Control ◽  
Language Profiles

ABSTRACT OBJECTIVE The purpose of this investigation was to determine whether clinical speech deficits after brain injury are associated with functional speech reorganization. METHODS Across an 18-year interval, 11 patients with mild-to-moderate speech deficits underwent language mapping as part of their treatment for intractable epilepsy. These “aphasics” were compared with 14 matched “control” patients with normal speech who also were undergoing epilepsy surgery. Neuroanatomic data were compared with quantitative language profiles and clinical variables. RESULTS Cortical lesions were evident near speech areas in all aphasia cases. As expected, aphasic and control patients were distinguished by quantitative language profiles. The groups were further distinguished by the anatomic distribution of their speech sites. A significantly greater proportion of frontal speech sites was found in patients with previous brain injury, consistent with frontal site recruitment. The degree of frontal recruitment varied as a function of patient age at the time of initial brain injury; earlier injuries were associated with greater recruitment. The overall number of speech sites remained the same after injury. Significant associations were found between the number of the speech sites, naming fluency, and the lesion proximity in the temporal lobe. CONCLUSION Language maps in aphasics demonstrated evidence for age-dependent functional recruitment in the frontal, but not temporal, lobe. The proximity of cortical lesions to temporal speech sites predicted the overall extent of temporal lobe speech representation and performance on naming fluency. These findings have implications for neurosurgical planning in patients with preoperative speech deficits.

A COLLABORATIVE VIRTUAL REALITY ENVIRONMENT FOR NEUROSURGICAL PLANNING AND TRAINING

2007 ◽  
Vol 61 ◽  
pp. 379-391 ◽  
Author(s):  
Ralf A. Kockro ◽  
Axel Stadie ◽  
Eike Schwandt ◽  
Robert Reisch ◽  
Cleopatra Charalampaki ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Virtual Reality Environment ◽  
Collaborative Virtual Reality ◽  
Neurosurgical Planning ◽  
And Training

Supervised Dimensionality Reduction of Single-cell Features from Label-free Multimodal Images for T Cell Subtype Classification

2021 ◽  
Author(s):  
Elizabeth Lee ◽  
Maciej Baranski ◽  
Marvin Chew ◽  
Wei-Xiang Sin ◽  
Ka-Wai Cheung ◽  
...  
Keyword(s):  
T Cell ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Label Free ◽  
Cell Subtype ◽  
Subtype Classification ◽  
Multimodal Images

Multimodal Risk-Based Path Planning for Neurosurgical Interventions

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Christian Kunz ◽  
Maximilian Gerst ◽  
Pit Henrich ◽  
Max Schneider ◽  
Michal Hlavac ◽  
...  
Keyword(s):  
User Interaction ◽  
Qualitative Evaluation ◽  
Modular Architecture ◽  
Holistic Model ◽  
Planning Time ◽  
Inherent Risk ◽  
Complex Anatomy ◽  
Neurosurgical Planning ◽  
Quantitative Performance ◽  
The Brain

Abstract Image-guided neurosurgical interventions are challenging due to the complex anatomy of the brain and the inherent risk of damaging vital structures. This paper presents a neurosurgical planning tool for safe and effective neurosurgical interventions, minimizing the risk through optimized access planning. The strengths of the proposed system are the integration of multiple risk structures combined into a holistic model for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons to quickly determine the most appropriate surgical entry point and trajectory through the brain with minimized risk. The user interface guides a user through the decision-making process and may save planning time of neurosurgical interventions. The navigation tool has been interfaced to the Robot Operating System, which allows the integration into automated workflows and the planning of linear and nonlinear trajectories. Determined risk structures and trajectories can be visualized intuitively as a projection map on the skin or cortical surface. Two risk calculation modes (strict and joint) are offered to the neurosurgeons, depending on the intracranial procedure's type and complexity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system.

NSCT-DCT based Fourier Analysis for Fusion of Multimodal Images

2021 ◽  
Author(s):  
Mayukhmala Jana ◽  
Subhosri Basu ◽  
Arpita Das
Keyword(s):  
Fourier Analysis ◽  
Multimodal Images

DTI and Tractography in Neurosurgical Planning

Diffusion MRI ◽  
2010 ◽  
pp. 588-607
Author(s):  
Christopher A. Clark ◽  
Tiernan Byrnes
Keyword(s):  
Neurosurgical Planning

Review of Fuzzy Image Segmentation Techniques

2011 ◽  
pp. 282-313 ◽  
Author(s):  
Gour C. Karmakar ◽  
Laurence Dooley ◽  
Mahbubhur Rahman Syed
Keyword(s):  
Image Segmentation ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Clustering Methods ◽  
Comprehensive Overview ◽  
Domain Specific ◽  
Soft Computing Techniques ◽  
Fuzzy Clustering Methods ◽  
Fuzzy Image ◽  
Multimodal Images

This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

VOLUME VISUALIZATION FOR SURGICAL PLANNING SYSTEM

2007 ◽  
Vol 07 (01) ◽  
pp. 55-63 ◽  
Author(s):  
CHAN CHEE FATT ◽  
IRWAN KASSIM ◽  
CHARLES LO ◽  
IVAN NG ◽  
KWOH CHEE KEONG
Keyword(s):  
Computer Technology ◽  
Surgical Approach ◽  
Surgical Planning ◽  
Volume Visualization ◽  
Planning System ◽  
Neurosurgical Planning ◽  
2D Imaging ◽  
3D Volume ◽  
Volume Models

The 3D volume visualization is to overcome the difficulties of the 2D imaging by using computer technology. A volume visualization approach has been successfully implemented for Surgical Planning System in National Neuroscience Institute (NNI). The system allows surgeons to plan a surgical approach on a set of 2D image slices and process into volume models and visualise them in 3D rapidly and interactively on PC. In our implementation, we have applied it in neurosurgical planning. The surgeon can visualize objects of interest like tumor and surgical path, and verify that the surgical plan avoids the critical features and the planning of the surgical path can thus be optimal.

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