An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images.

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

An interpretable feature-learned model for overall survival classification of High-Grade Gliomas.

PURPOSE: An accurate and well-defined survival prediction of High Grade Gliomas (HGGs) is indispensable because of its high incidence and aggressiveness. Therefore, this paper presents a unified framework for fully automatic overall survival classification and its interpretation. METHODS AND MATERIALS: Initially, a glioma detection model is utilized to detect the tumorous images. A pre-processing module is designed for extracting 2D slices and creating a survival data array for the classification network. Then, the classification pipeline is integrated with two separate pathways: a modality-specific and a modality-concatenated pathway. The modality-specific pathway runs three separate CNNs for extracting rich predictive features from three sub-regions of HGGs (peritumoral edema, enhancing tumor and necrosis) by using three neuro-imaging modalities. In these pathways, the image vectors of the different modalities are also concatenated to the final fusion layer to overcome the loss of lower-level tumor features. Furthermore, to exploit the intra-modality correlations, a modality-concatenated pathway is also added to the classification pipeline. The experiments are conducted on BraTS 2018 and BraTS 2019 benchmarks, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors. RESULTS: The proposed approach achieves an overall classification accuracy, sensitivity, and specificity of about 0.998, 0.997, and 0.999, respectively, for the BraTS 2018 dataset, and for BraTS 2019, these values correspond to 1.000, 0.999, and 0.999. CONCLUSIONS: The results indicate that the proposed model achieves the highest values of the evaluation metrics for the overall survival classification of HGG.

Cognitive remediation and schizophrenia: Effects on brain complexity.

The objective of this study is to investigate nonlinear neural dynamics of chronic patients with schizophrenia following 3 months of cognitive remediation and to find correlations with neuropsychological measures of cognition. Twenty nine patients were randomized to Cognitive Training (CT) and Treatment as Usual (TAU) group. The system complexity is estimated by Correlation Dimension (D2) and Largest Lyapunov Exponent (LLE) from the reconstructed attractor of the underlying system. Significant increase in dimensional complexity (D2) over time is observed in prefrontal and medial frontal-central regions in eyes open and arithmetic condition; and posterior parietal-occipital region under eyes closed after 3 months. Dynamical complexity (LLE) significantly decreased over time in medial left central region under eyes closed and eyes open condition; prefrontal region in eyes open and lateral right temporal region in arithmetic condition. Interaction is significant for medial left central region with TAU group exhibiting greater decrease in LLE compared to CT group. The CT group showed significant correlation of increased D2 with focused attention. In this study it is found that patients with schizophrenia exhibit higher dimensional and lower dynamical complexity over time indicating improvement in neurodynamics of underlying physiological system.

Ecological memory of prior nutrient exposure in the human gut microbiome.

Many ecosystems have been shown to retain a memory of past conditions, which in turn affects how they respond to future stimuli. In microbial ecosystems, community disturbance has been associated with lasting impacts on microbiome structure. However, whether microbial communities alter their response to repeated stimulus remains incompletely understood. Using the human gut microbiome as a model, we show that bacterial communities retain an "ecological memory" of past carbohydrate exposures. Memory of the prebiotic inulin was encoded within a day of supplementation among a cohort of human study participants. Using in vitro gut microbial models, we demonstrated that the strength of ecological memory scales with nutrient dose and persists for days. We found evidence that memory is seeded by transcriptional changes among primary degraders of inulin within hours of nutrient exposure, and that subsequent changes in the activity and abundance of these taxa are sufficient to enhance overall community nutrient metabolism. We also observed that ecological memory of one carbohydrate species impacts microbiome response to other carbohydrates, and that an individual's habitual exposure to dietary fiber was associated with their gut microbiome's efficiency at digesting inulin. Together, these findings suggest that the human gut microbiome's metabolic potential reflects dietary exposures over preceding days and changes within hours of exposure to a novel nutrient. The dynamics of this ecological memory also highlight the potential for intra-individual microbiome variation to affect the design and interpretation of interventions involving the gut microbiome.

Comparison of Effect of Non-invasive Ventilation Delivered by Helmet vs Face Mask in Patients with COVID-19 Infection: A Randomized Control Study.

Background and aims: We compared the effectiveness of non-invasive ventilation (NIV) provided by helmet mask vs face mask in patients with COVID-19. Methods and materials: Between March and May 2021, a single-center, prospective, open-label randomized controlled research was undertaken. Sixty patients were randomly assigned to one of two groups based on the NIV delivery interface. In group I (n = 30) helmet mask was used and in group II (n = 30) face mask was used for delivery of NIV. The proportion of patients in each group who required endotracheal intubation was the primary outcome. The duration of NIV, length of stay in the intensive care unit (ICU), hospital mortality, ratio of partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2), respiratory rate, patient comfort, and complications were all documented as secondary outcomes. Results: In both groups, demographics, clinical characteristics, and treatment received were comparable. Around 10% of patients in the helmet mask group were intubated, while 43.3% of patients in the face mask group were intubated (p = 0.004). The two groups demonstrated similar hemodynamic patterns. The use of a helmet mask, on the other hand, resulted in enhanced oxygenation (263.57 ± 31.562 vs 209.33 ± 20.531, p = 0.00), higher patient satisfaction (p = 0.001), a lower risk of complications, and a shorter NIV and ICU stay (p = 0.001) (4.53 ± 0.776 vs 7.60 ± 1.354, p = 0.00 and 6.37 ± 0.556 vs 11.57 ± 2.161, p = 0.00). Conclusion: Helmet mask could be a reliable interface for delivery of NIV in COVID-19 and results in a lower rate of endotracheal intubation, better oxygenation with greater patient comfort and shorter ICU stay as compared to face mask used for NIV. How to cite this article: Saxena A, Nazir N, Pandey R, Gupta S. Comparison of Effect of Non-invasive Ventilation Delivered by Helmet vs Face Mask in Patients with COVID-19 Infection: A Randomized Control Study. Indian J Crit Care Med 2022;26(3):282-287.

Monolithic Zirconia Partial Coverage Restorations: An In Vitro Mastication Simulation Study.

PURPOSE: To evaluate the survival rate (fatigue resistance), bonding efficiency and marginal integrity of monolithic zirconia partial and full coverage single restorations adhesively bonded to the tooth structure using air-particle abrasion, a primer with 10-methacryloyloxydecyl dihydrogen phosphate and a composite-resin cement (APC) protocol. MATERIALS AND METHODS: Extracted human premolars (N = 32) were randomly divided into four groups of eight specimens each. Premolars were prepared for the following restorations: full crown (group 1, control), mesial-occlusal-distal-facial onlay (MODF, group 2) preserving 2 mm facio-lingual functional cusp width, mesial-occlusal-distal-lingual onlay (MODL, group 3) preserving 2 mm facio-lingual nonfunctional cusp width, mesial-occlusal-distal-buccal-lingual onlay (MODBL, group 4), overlay preparation. All restorations were milled from monolithic 3 mol% yttria (3Y) zirconia blocks (ZirCad, A1 LT, Ivoclar Vivadent) with CAD/CAM software presets at minimum occlusal and axial thicknesses of 1 mm. The intaglio surface of the restorations was air-particle abraded (50 µm Al2 O3 , 2-Bar pressure, 15 s, 10 mm distance) and primed. An adhesive cement system was used to bond the restorations. Each group was subjected to thermomechanical loading for 1.2 million cycles (force = 70 N, 1.4 Hz) with simultaneous thermocycling (5-55°C, 30 s dwell time) using a mastication simulator. All specimens were examined under scanning electron microscope (SEM) analysis (30, 100, and 150×) to evaluate cracks and marginal defects. Fracture of restoration and/or fracture within tooth structure, and debonding were considered modes of failure. RESULTS: One specimen from group 2 debonded at 632,000 cycles. None of the specimens failed due to fracture. SEM analysis at 30× indicated marginal integrity issue of the remaining seven intact specimens of group 2 in the area of antagonist contact. No specimens from group 1, 3, and 4 demonstrated marginal integrity issue at 30×. None of the specimens demonstrated any microcrack at 100× and150×. CONCLUSIONS: Due to its fatigue resistance, 3Y-zirconia is a viable option for partial and full coverage single restorations. Following a strict bonding protocol, zirconia demonstrated durable adhesion to the tooth structure. Occlusal contact on restoration margins should be avoided.

Automatic segmentation of tumors in B-Mode breast ultrasound images using information gain based neutrosophic clustering.

BACKGROUND: Since breast ultrasound images are of low contrast, contain inherent noise and shadowing effect due to its imaging process, segmentation of breast tumors depicting ultrasound image is a challenging task. Thus, a robust breast ultrasound image segmentation technique is inevitable. OBJECTIVE: To develop an automatic lesion segmentation technique for breast ultrasound images. METHODS: First, the technique automatically detects the suspicious tumor region of interest and discards the unwanted complex background regions. Next, based on the concept of information gain, the technique applies an existing neutrosophic clustering method to the detected region to segment the desired tumor area. The proposed technique computes information gain values from the local neighbourhood of each pixel, which is further used to update the membership values and the cluster centers for the neutrosophic clustering process. Integrating the concept of entropy and neutrosophic logic features into the technique enabled to generate better segmentation results. RESULTS: Results of proposed method were compared both qualitatively and quantitatively with fuzzy c-means, neutrosophic c-means and neutrosophic ℓ-means clustering methods. It was observed that the proposed method outperformed the other three methods and yielded the best Mean (TP: 94.72, FP: 5.85, SI: 93.75, HD: 8.2, AMED: 2.4) and Standard deviation (TP: 3.2, FP: 3.7, SI: 3.8, HD: 2.6, AMED: 1.3) values for different quality metrics on the current set of breast ultrasound images. CONCLUSION: Study demonstrated that the proposed technique is robust to the shadowing effect and produces more accurate segmentation of the tumor region, which is very similar to that visually segmented by Radiologist.

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

Comparison of analgesic efficacy of caudal dexmedetomidine versus caudal tramadol with ropivacaine in paediatric infraumbilical surgeries: A prospective, randomised, double-blinded clinical study.

BACKGROUND AND AIMS: Caudal epidural analgesia is commonly practised regional block technique in children undergoing infraumbilical surgeries but has a short duration of action after single shot local anaesthetic injection. The aim of this study was to compare ropivacaine 0.25% with dexmedetomidine and tramadol in caudal anaesthesia in paediatric infraumbilical surgeries. METHODS: In a randomised, prospective, double-blinded study, sixty children (1-8 years) belonging to American Society of Anesthesiologists' physical status I or II scheduled for infraumbilical surgeries were included. They were randomly assigned into two groups: Group ropivacaine with tramadol (RT) (n = 30) received 0.25% ropivacaine 1 mL/kg with 2 mg/kg of tramadol, and Group ropivacaine with dexmedetomidine (RD) (n = 30) received 0.25% ropivacaine 1 mL/kg with dexmedetomidine 2 µg/kg. The primary outcome variable was the duration of analgesia, and the secondary outcome variables included motor block, sedation score, time from caudal block to skin incision, emergence time and adverse effects. RESULTS: The mean duration of analgesia was 654.20 ± 78.38 min in Group RT, while in Group RD, it was 780.29 ± 71.21 min (P = 0.0001). The difference between the mean sedation score and mean emergence time between the two groups were statistically significant (P = 0.0001 and 0.0411, respectively). No significant difference was observed in the incidence of haemodynamic changes or side effects. CONCLUSION: Caudal dexmedetomidine with ropivacaine prolongs post-operative analgesia compared to caudal tramadol with ropivacaine.

Nakagami-based total variation method for speckle reduction in thyroid ultrasound images.

A good statistical model is necessary for the reduction in speckle noise. The Nakagami model is more general than the Rayleigh distribution for statistical modeling of speckle in ultrasound images. In this article, the Nakagami-based noise removal method is presented to enhance thyroid ultrasound images and to improve clinical diagnosis. The statistics of log-compressed image are derived from the Nakagami distribution following a maximum a posteriori estimation framework. The minimization problem is solved by optimizing an augmented Lagrange and Chambolle's projection method. The proposed method is evaluated on both artificial speckle-simulated and real ultrasound images. The experimental findings reveal the superiority of the proposed method both quantitatively and qualitatively in comparison with other speckle reduction methods reported in the literature. The proposed method yields an average signal-to-noise ratio gain of more than 2.16 dB over the non-convex regularizer-based speckle noise removal method, 3.83 dB over the Aubert-Aujol model, 1.71 dB over the Shi-Osher model and 3.21 dB over the Rudin-Lions-Osher model on speckle-simulated synthetic images. Furthermore, visual evaluation of the despeckled images shows that the proposed method suppresses speckle noise well while preserving the textures and fine details.

A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features.

Stroke is the third major cause of death worldwide behind heart disease and cancer. Carotid atherosclerosis is the most frequent cause of ischemic stroke. Early diagnosis of carotid plaque and serial monitoring of its size with the help of imaging modalities can help to prevent the atherosclerotic complications. The main difficulty is inevitable variability of patient's head positions during image acquisitions. The time series registration of carotid images helps to improve the monitoring, characterization, and quantification of the disease by suppressing the global movements of the patient. In this work, a novel hybrid registration technique has been proposed and evaluated for registration of carotid ultrasound images taken at different times. The proposed hybrid method bridges the gap between the feature-based and intensity-based registration methods. The feature-based iterative closest point algorithm is used to provide a coarse registration which is subsequently refined by the intensity-based algorithm. The proposed framework uses rigid transformation model coupled with mutual information (MI) similarity measure and Powell optimizer. For quantitative evaluation, different registration approaches have been compared using four error metrics: visual information fidelity, structural similarity index, correlation coefficient, and MI. Qualitative evaluation has also been done using the visual examination of the registered image pairs.

Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound.

Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform-based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%.

Correlation of fine needle aspiration cytology with histopathology in the diagnosis of solitary thyroid nodule.

Background. Fine needle aspiration cytology is considered the gold standard diagnostic test for the diagnosis of thyroid nodules. Fine needle aspiration cytology is a cost effective procedure that provides specific diagnosis rapidly with minimal complications. Based on the cytology findings, patients can be followed in cases of benign diagnosis and subjected to surgery in cases of malignant diagnosis thereby decreasing the rate of unnecessary surgery. Purpose of the present study was to correlate the fine needle aspiration cytology findings with histopathology of excised specimens. Material and Methods. This was a prospective study conducted on 75 consecutive patients between January 2003 and December 2005. All patients with clinically diagnosed solitary thyroid nodule who were clinically and biochemically euthyroid were included for study. Patients with multinodular goitre and who were hypothyroid or hyperthyroid were excluded from the study. Results. The sensitivity, specificity, accuracy, false positive rate, false negative rate, positive predictive value, and negative predictive value of FNAC for the diagnosis of neoplastic solitary thyroid nodules were 80%, 86.6%, 13.3%, 20%, 80%, and 86.6%, respectively. Commonest malignancy detected was papillary carcinoma in 12 patients. Conclusions. Fine needle aspiration cytology is a simple, easy to perform, cost effective, and easily repeated procedure for the diagnosis of thyroid cancer. It is recommended as the first line investigation for the diagnosis of solitary thyroid nodule.