Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

Automated facial characterization and image retrieval by convolutional neural networks.

Introduction: Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks. Methods: We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation. Results and discussion: Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis.

The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak.

Quantitative auto-fluorescence quenching of free and bound NADH in HeLa cell line model with Carbonyl cyanide-p-Trifluoromethoxy phenylhydrazone (FCCP) as quenching agent.

The autofluorescence of endogenous biomolecules (Nicotinamide adenine dinucleotide (NAD, its reduced form NADH and the phosphorylated form NAD(P)H take part in cellular metabolic pathways and has vital importance for in vivo and ex vivo photo diagnostic applications of biological tissues. We present a detailed quenching analysis of Carbonyl cyanide-p-Trifluoromethoxy phenylhydrazone (FCCP) 50-1000 µM and analyzed the fluorescence signal from NADH/ NAD(P)H in vitro (in solution) and in vivo (HeLa cell suspension).The in vitro samples of pure NADH/ NAD(P)H were excited at λ=340±1 nm while the fluorescence signal was collected in the range of 400-550 nm. The quenching process was characterized using excitation emission matrix (EEM) fluorescence spectroscopy and Stern- Volmer plots. The experimental results illustrated maximum fluorescence emission for the control NADH samples (i.e., no FCCP), while the fluorescence signal from the solution progressively decreased with the increasing concentration of the FCCP, until it reaches the base line (i.e., no fluorescence signal) at 1000 µM of FCCP. In vitro study shows that the fluorescence quenching of free NADH was found to be lower than the bound NAD(P)H with similar diminishing trend. The quenching of bound NAD(P)H in cells is attenuated compared to solution quenching possibly due to a contribution from the metabolic/antioxidant response in cells and fluorescence exponential decay curve lies between plated and suspended HeLa cells. A two-fold increase in the fluorescence intensity of NAD(P)H was observed after the bond formation with L-Malate Dehydrogenase (L-MDH, Sigma Aldrich #10127248001) protein This work has applications for sharp tumor demarcation during sensitive surgical procedures as well as to enhance fluorescence based diagnosis of biological tissues.

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography.

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues.

Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.

Mitosis detection in breast cancer histopathology images using hybrid feature space.

Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.

Biomedical Applications of Integrating Sphere: A Review.

Integrating sphere remains a valuable tool for biomedical optics research. Specifically, it has been used to determine the optical properties (i.e., absorption coefficient, scattering coefficient and anisotropy factor) of biological tissues. This study presents an overview of the literature on integrating sphere and its biomedical applications. In particular, we start with a brief introduction of tissue optics with emphasis on the optical properties of biological tissues, followed by a detailed discussion of the hardware and related procedures of the integrated sphere system. Both the experimental procedure and subsequent analytical models (i.e., first order scattering, Kubelka Munk, diffusion approximation, Monte Carlo and inverse adding-doubling methods) along with illustrative examples are also outlined. Finally, illustrative examples to explore the optical properties of tissue phantoms and biological samples have been discussed. This survey will provide a ready reference and overview for the applications of integrating sphere in biomedical optics.

Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain.

This work presents a diagnostic system for the hepatitis C infection using Raman spectroscopy and proximity based classification. The proposed method exploits transformed Raman spectra using the proximity based machine learning technique and is denoted as RS-PCA-Prox. First, Raman spectral data is baseline corrected by subtracting noise and low intensity background. After this, a feature transformation of Raman spectra is adopted, not only to reduce the feature's dimensionality but also to learn different deviations in Raman shifts. The proposed RS-PCA-Prox shows significant diagnostic power in terms of accuracy, sensitivity, and specificity as 95%, 0.97 and 0.94 in PCA based transformed domain. The comparison of the RS-PCA-Prox with linear and ensemble based classifiers shows that proximity based classification performs better for the discrimination of HCV infected individuals and is able to differentiate the infected individuals from normal ones on the basis of molecular spectral information. Furthermore, it is observed that characteristic spectral changes are due to variation in the intensity of lectin, chitin, lipids, ammonia and viral protein as a consequence of the HCV infection.

Hybrid diversification operator-based evolutionary approach towards tomographic image reconstruction.

The proposed algorithm introduces a new and efficient hybrid diversification operator (HDO) in the evolution cycle to improve the tomographic image reconstruction and diversity in the population by using simulated annealing (SA), and the modified form of decreasing law of mutation probability. This evolutionary approach has been used for parallel-ray transmission tomography with the head and lung phantoms. The algorithm is designed to address the observation that the convergence of a genetic algorithm slows down as it evolves. The HDO is shown to yield a higher image quality as compared with the filtered back-projection (FBP), the multiscale wavelet transform, the SA, and the hybrid continuous genetic algorithm (HCGA) techniques. Various crossover operators including uniform, block, and image-row crossover operators have also been analyzed, and the latter has been generally found to give better image quality. The HDO is shown to yield improvements of up to 92% and 120% when compared with FBP in terms of PSNR, for 128 × 128 head and lung phantoms, respectively.