GEN-8 Real-time PCR based intraoperative genetic analysis for gliomas

An individual therapeutic strategy based on the genetic characterization is important in gliomas. However, it has been difficult to obtain genetic features during surgery. In this study, we present an overview of intraoperative genetic analysis using modified real-time PCR method. The tumor specimen was crushed with liquid nitrogen, then extract DNA within 60 minutes. Reagents of real-time PCR for detecting IDH, TERT, and BRAF hot spot mutations were stocked and real-time PCR was performed after mixing the extracted DNA. We used PNA and LNA to detect single nucleotide variant (SNV). The average time from tumor extraction to intraoperative tentative judgement was approximately 100 minutes. Using this system. we preliminary performed intraoperative genomic analysis in10 glioma patients. We confirmed that 8 of 10 cases (80%) of intraoperative genomic diagnosis were consistent with post-operative diagnosis by Sanger sequencing. However, we experienced 2 (20%) unmatched cases due to low allele of SNV, which indicates that more advanced system is required for clinical application.

Preprocessing and Skull Stripping of Brain Tumor Extraction from Magnetic Resonance Imaging Images Using Image Processing

Many neuroimaging processing functions believe the preprocessing and skull strip (SS) to be an important step in brain tumor diagnosis. For complex physical reasons intensity changes in brain structure and magnetic resonance imaging of the brain, a proper preprocessing and SS is an important part. The method of removing the skull is relayed to the taking away of the skull area in the brain for medical investigation. It is more correct and necessary techniques for distinguishing between brain regions and cranial regions and this is believed a demanding task. This paper gives detailed review on the preprocessing and traditional transition to machine learning and deep learning-based automatic SS techniques of magnetic resonance imaging.

Brain Tumor Extraction Using Matlab

The brain is the most crucial part of our human body which acts as central coordinating system for all the controlling and all regular functions of our body. The continuous growth of abnormal cells which creates certain mass of tissue is called as tumor. Tumor in the brain can be either formed inside the brain or gets into brain after formed at other part. But there is no clear information regarding the formation of brain tumor till date. Though the formation tumor in brain is not common or regular but the mortality rate of the infected people is very high because the brain is major part of body. So, it is very important get the treatment at the early stages of brain tumor but there is no direct procedure for detection and classification of tumor in the very first step of diagnosis. In actual medical diagnosis, mri images alone can’t be able to determine the detected tumor as either the cancerous or non-cancerous. But the tumor may be sometimes danger to life or may not be danger to life. Tumor inside the brain can be of either the benign(non- cancerous) or the malignant(cancerous). So, we need to detect the tumor from the MRI images through image processing and then to classify the detected tumor as it belongs to either the benign or malignant tumor. We are going to get the brain mri images as our dataset for our proposed method but the images we got may have the noise. So, we need to preprocess the image using the image preprocessing techniques. We are going to use several algorithms like thresholding, clustering to make the detection of tumor by using the image processing and image segmentation and after the detection of tumor we are going do feature extraction. This step involves the extraction of detected objects features using DWT. This extracted features are given as input to classifier algorithms like SVM’s and CNN after reduction of features using the PCA.

Brain Tumor Detection using Image Processing

Brain tumor extraction and its analysis are challenging tasks in medical image processing because brain image is complicated .Segmentation plays a very important role in the medical image processing.In that way MRI (magnetic resonance imaging )has become a useful medical diagnostic tool or the diagnosis o brain & other medical images.In this project, we are presenting a comparative study of three segmentation methods implemented or tumor detection .The method includes kmeans clustering using watershed algorithm . Optimized k-means and optimized c-means using genetic algorithm.

Tailored Optic Unroofing for Type III Clinoidal Meningioma: Description of a Surgical Technique

Background: Anterior clinoidal meningiomas are heterogeneous types of lesions that comprise the parasellar lesions group. Due to their close relationship with the optic nerve and internal carotid artery, they become challenging pathologies for neurosurgeons. Case Description: Female 47, presented with superior temporal quadrantanopsia on the right side. Magnetic resonance image revealed type III clinoidal meningioma on the right side. She has undergone a pterional craniotomy for an optic canal unroofing and tumor extraction. Two years of postoperative follow-up the patient underwent a campimetry, which revealed an almost complete visual improvement. Conclusion: To date, the best surgical technique has not yet been defined, so the choice of treatment and surgical technique is based on each case and on the surgeons’ experience.

Brain Tumor Extraction from MRI Images Using MATLAB

Computerized or Automatic detection of tumors in medical images is motivated by the necessity of high accuracy when dealing with a human life. The computer assistance is also demanded in medical institutions because it could improve the results of disease identification and negative cases should be very low. So, the Processing of Magnetic Resonance Imaging (MRI) images is one of the techniques to diagnose the brain tumor. This paper describes the strategy to detect and extract brain tumor from patient’s MRI scanned images. In this the Steps includes are pre-processing, segmentation, morphological operation, watershed segmentation and calculation of the tumor area and determination of the tumor location and this Application is Developed using Matrix Laboratory (MATLAB)

Role of General Adversarial Networks in Mammogram Analysis: A Review

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.