SegNet and Salp Water Optimization-driven Deep Belief Network for Segmentation and Classification of Brain Tumor

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.     

Magnetic Resonance Imaging Conditional Pacemakers: Rationale, Development and Future Directions

Pacemakers and other cardiac implantable electronic devices (CIEDs) have long been considered an absolute contraindication to magnetic resonance imaging (MRI), a crucial and growing imaging modality. In the last 20 years, protocols have been developed to allow MR scanning of CIED patients with a low complication rate. However, this practice has remained limited to a relatively small number of centers, and many pacemaker patients continue to be denied access to clinically indicated imaging. The introduction of MRI conditional pacemakers has provided a widely applicable and satisfactory solution to this problem. Here, the interactions of pacemakers with the MR environment, the results of MR scanning in patients with conventional CIEDs, the development and clinical experience with MRI conditional devices, and future directions are reviewed.     

A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography

BackgroundDiabetic retinopathy (DR) is one of the major causes of blindness in adults suffering from diabetes. With the development of wide-field optical coherence tomography angiography (WF-OCTA), it is to become a gold standard for diagnosing DR. The demand for automated DR diagnosis system based on OCTA images have been fostered due to large diabetic population and pervasiveness of retinopathy cases.Materials and methodsIn this study, 288 diabetic patients and 97 healthy people were imaged by the swept-source optical coherence tomography (SS-OCT) with 12 mm × 12 mm single scan centered on the fovea. A multi-branch convolutional neural network (CNN) was proposed to classify WF-OCTA images into four grades: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR).ResultsThe proposed model achieved a classification accuracy of 96.11%, sensitivity of 98.08% and specificity of 89.43% in detecting DR. The accuracy of the model for DR staging is 90.56%, which is higher than that of other mainstream convolution neural network models.ConclusionThis technology enables early diagnosis and objective tracking of disease progression, which may be useful for optimal treatment to reduce vision loss.     

MRI在甲状腺肿瘤的肿块定位和良恶性判断中的应用研究

目的:总结甲状腺肿瘤的核磁共振成像(MRI)影像学表现,评价MRI在甲状腺肿瘤的肿块定位及良恶性判断中的应用价值。方法:选取我院外科收治的经MRI诊断为甲状腺癌的患者60例,总结所有患者的影像学资料,并将其与诊断金标准(病理学检查结果)相比较,分析MRI检查诊断累及组织和鉴别良恶性甲状腺肿瘤的准确率等。结果:MRI影像学结果清晰可见甲状腺肿瘤的部位、形态、密度、边界及是否有钙化等重要征象;MRI对良恶性肿瘤诊断准确性为95.0%。MRI对肿瘤累积膜外脂肪、肿瘤的淋巴结以及肿瘤的食管转移均有较高的准确性、敏感性与特异性。结论:MRI影像学检查清晰可见甲状腺肿瘤的部位、形态、密度、边界及是否有钙化等重要征象,对临床上甲状腺肿瘤的肿块定位及良恶性鉴别诊断具有重要的参考意义。     

BASIN: A Semi-automatic Workflow,with Machine Learning Segmentation,for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets

Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers’ eyes to draw conclusions. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an objective and reproducible comparison leveraging inferential statistics to bridge image data with other modalities. Users have access to machine learning-based object segmentation. BASIN provides several data points such as images’ object counts, intensities, and areas. Hypothesis testing may also be performed. To improve BASIN’s accessibility, we implemented it using R Shiny and provided both an online and offline version. We used BASIN to process 498 image pairs involving five bioscience topics. Our framework supported either direct claims or extrapolations 57% of the time. Analysis results were manually curated to determine BASIN’s accuracy which was shown to be 78%. Additionally, each BASIN version’s initial release shows an average 82% FAIR compliance score.     

A Slantlet transform based intelligent system for magnetic resonance brain image classification

The present paper proposes the development of a new approach for automated diagnosis, based on classification of magnetic resonance (MR) human brain images. Wavelet transform based methods are a well-known tool for extracting frequency space information from non-stationary signals. In this paper, the proposed method employs an improved version of orthogonal discrete wavelet transform (DWT) for feature extraction, called Slantlet transform, which can especially be useful to provide improved time localization with simultaneous achievement of shorter supports for the filters. For each two-dimensional MR image, we have computed its intensity histogram and Slantlet transform has been applied on this histogram signal. Then a feature vector, for each image, is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions, chosen according to a specific logic. The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from Alzheimer's disease. An excellent classification ratio of 100% could be achieved for a set of benchmark MR brain images, which was significantly better than the results reported in a very recent research work employing wavelet transform, neural networks and support vector machines.     

MR 脑图像海马自动分割法在AD 早期诊断中的应用研究

目的:研究磁共振(Magnetic resonance,MR)脑图像中海马的自动分割方法及海马的形态学分析方法,为阿尔茨海默病(Alzheimer’s disease,AD)的早期诊断提供依据。方法:对20例AD患者和60名正常对照者行MRI T1 WI 3D容积扫描,建立海马的三维主动表观模型,并以此模型对每个个体脑部磁共振图像上的海马进行自动识别和三维分割,分别建立正常对照组和AD组的海马统计形状模型,比较AD组与正常对照组间海马形状的差异性。结果:海马三维分割方法与手动分割方法在海马体积测量上无统计学差别(P>0.05);AD患者海马头部发生萎缩(P<0.05)。结论:基于主动表观模型的MR脑图像海马自动识别和三维分割法是准确可靠的;海马头部萎缩可作为AD诊断的依据之一。     

Lesion Explorer: A Video-guided,Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer''s disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests^1,2. However, LE''s accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer''s manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE''s manual procedures.