Framework for a low-cost intra-operative image-guided neuronavigator including brain shift compensation

In this paper we present a methodology to address the problem of brain tissue deformation referred to as 'brain-shift'. This deformation occurs throughout a neurosurgery intervention and strongly alters the accuracy of the neuronavigation systems used to date in clinical routine which rely solely on pre-operative patient imaging to locate the surgical target, such as a tumour or a functional area. After a general description of the framework of our intra-operative image-guided system, we describe a procedure to generate patient specific finite element meshes of the brain and propose a biomechanical model which can take into account tissue deformations and surgical procedures that modify the brain structure, like tumour or tissue resection

Content-Based Image Retreival for Detecting Brain Tumors and Amyloid Fluid Presence

Medical images play a vital role in identifying diseases and detecting if organs are functioning correctly. Image processing related to medical images is an active research area in which various techniques are used in order to make diagnosis easier. The brain is a vital organ in our body, and brain tumors are a very critical life altering condition. Identifying tumors is a challenging task and various image processing techniques can be used. Doctors can identify tumors from looking at the scan, and this project attempts to automatically derive these results. In this project, image processing is done for automatically detecting the presence of brain tumors in a given brain scan. Content-based image retrieval extracts features from a query or template image, computes a measure of similarity, and gives results by detecting tumors. Template matching is used to identify a template at any position within the image to identify tumor location. Secondly, early detection of Alzheimer’s, which in turn prevents dementia, can be determined from the presence of amyloid fluid along with the other factors. The amyloid fluid presence helps in detecting dementia at an early stage. The presence of this fluid can be found in a PET scan of the brain. Here, the idea is to show the color distribution from a scan image, i.e., the domination of given colors. Content-based image retrieval’s low level feature based approaches such as color histograms are used. In this project, the conventional K- means algorithm is used for clustering the histograms, and identifying dominant colors

Automatic finite elements mesh generation from planar contours of the brain: an image driven 'blobby' approach

In this paper, we address the problem of automatic mesh generation for finite elements modeling of anatomical organs for which a volumetric data set is available. In the first step a set of characteristic outlines of the organ is defined manually or automatically within the volume. The outlines define the "key frames" that will guide the procedure of surface reconstruction. Then, based on this information, and along with organ surface curvature information extracted from the volume data, a 3D scalar field is generated. This field allows a 3D reconstruction of the organ: as an iso-surface model, using a marching cubes algorithm; or as a 3D mesh, using a grid "immersion" technique, the field value being used as the outside/inside test. The final reconstruction respects the various topological changes that occur within the organ, such as holes and branching elements

Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions

A major challenge in stroke research and stroke recovery predictions is the determination of a stroke lesion's extent and its impact on relevant brain systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR) imaging volumes, the current gold standard, is not only very time-consuming, but its accuracy highly depends on the operator's experience. As a result, there is a need for a fully automated segmentation method that can efficiently and objectively measure lesion extent and the impact of each lesion to predict impairment and recovery potential which might be beneficial for clinical, translational, and research settings. We have implemented and tested a fully automatic method for stroke lesion segmentation which was developed using eight different 2D-model architectures trained via transfer learning (TL) and mixed data approaches. Additionally, the final prediction was made using a novel ensemble method involving stacking and agreement window. Our novel method was evaluated in a novel in-house dataset containing 22 T1w brain MR images, which were challenging in various perspectives, but mostly because they included T1w MR images from the subacute (which typically less well defined T1 lesions) and chronic stroke phase (which typically means well defined T1-lesions). Cross-validation results indicate that our new method can efficiently and automatically segment lesions fast and with high accuracy compared to ground truth. In addition to segmentation, we provide lesion volume and weighted lesion load of relevant brain systems based on the lesions' overlap with a canonical structural motor system that stretches from the cortical motor region to the lowest end of the brain stem.Comment: 13 Pages, 5 figures, 3 table

Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts

ISSN:0897-1889ISSN:1618-727

Automatic MRI 2D Brain Segmentation using Graph SearchingTechnique

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stabilit