Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors

Challenging tasks such as lesion segmentation, classification, and analysis for the assessment of disease progression can be automatically achieved using deep learning (DL)-based algorithms. DL techniques such as 3D convolutional neural networks are trained using heterogeneous volumetric imaging data such as MRI, CT, and PET, among others. However, DL-based methods are usually only applicable in the presence of the desired number of inputs. In the absence of one of the required inputs, the method cannot be used. By implementing a generative adversarial network (GAN), we aim to apply multi-label automatic segmentation of brain tumors to synthetic images when not all inputs are present. The implemented GAN is based on the Pix2Pix architecture and has been extended to a 3D framework named Pix2PixNIfTI. For this study, 1,251 patients of the BraTS2021 dataset comprising sequences such as T1w, T2w, T1CE, and FLAIR images equipped with respective multi-label segmentation were used. This dataset was used for training the Pix2PixNIfTI model for generating synthetic MRI images of all the image contrasts. The segmentation model, namely DeepMedic, was trained in a five-fold cross-validation manner for brain tumor segmentation and tested using the original inputs as the gold standard. The inference of trained segmentation models was later applied to synthetic images replacing missing input, in combination with other original images to identify the efficacy of generated images in achieving multi-class segmentation. For the multi-class segmentation using synthetic data or lesser inputs, the dice scores were observed to be significantly reduced but remained similar in range for the whole tumor when compared with evaluated original image segmentation (e.g. mean dice of synthetic T2w prediction NC, 0.74 ± 0.30; ED, 0.81 ± 0.15; CET, 0.84 ± 0.21; WT, 0.90 ± 0.08). A standard paired t-tests with multiple comparison correction were performed to assess the difference between all regions (p < 0.05). The study concludes that the use of Pix2PixNIfTI allows us to segment brain tumors when one input image is missing

Truth Discovery in Big Data Social Media Application

In this system first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on, Instagram, rumors, Twitter scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. We are going develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above all challenges. In this, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach

High-dose-rate Interstitial Brachytherapy Boost with a Pedicled Latissimus Dorsi Myocutaneous Flap for Myxofibrosarcoma of the Arm

Summary: A 71-year-old man was found to have a 7.4 × 2.9 × 7.0 cm myxofibrosarcoma of the right medial arm close to neurovascular structures. He received 50 Gray (Gy) of preoperative external beam radiation. Radical resection resulted in a 15 × 10 cm defect. Nine brachytherapy catheters were placed, and a pedicled latissimus dorsi myocutaneous flap was used in reconstruction. Final pathology confirmed myxofibrosarcoma, high grade. The tumor was <1 mm from 2 margins. A total of 17.5 Gy of brachytherapy was delivered to the surgical bed from postoperative days 7 to 9. The flap developed fat necrosis distally which eventually required surgical debridement on postoperative day 58. It subsequently healed well and maintained good function of the limb. The patient remains under surveillance without evidence of recurrence

A survey on Data Extraction and Data Duplication Detection

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Processing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algorithms are needed to extract useful features from huge amount of data. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. This Paper review the literature on duplicate detection and data fusion (remov e and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

STUDIES ON BIOAVAILABILITY ENHANCEMENT OF CURCUMIN

Objective: The objective of the present work was to improve aqueous solubility and in vivo bioavailability of curcumin and structural analogues of curcumin such as potassium, calcium, magnesium salts and nitro derivative. Methods: Structural analogues of curcumin were prepared by reaction of curcumin with potassium chloride, magnesium chloride hexahydrate and calcium chloride dihydrate in a suitable solvent. The nitro derivative synthesized by treating curcumin with sulphuric acid and nitric acid. The prepared analogues were evaluated for melting behavior, solubility, UV spectrophotometry, partition coefficient, moisture content, cellular uptake, FTIR analysis, antimicrobial activity and in vivo bioavailability in the rat. Results: Chemical modification of curcumin increased the saturation solubility to 11.6, 16.5, 21.5, 28.0 µg/ml in calcium salt, magnesium salt, potassium salt and nitro derivative respectively, against 8.6 µg/ml of curcumin. The analogues were chemically stable as curcumin analyzed by FTIR spectrophotometry. Increased cellular uptake, as well as enhanced antimicrobial activity, was demonstrated by modified curcumin analogues. Moreover, significant improvement in plasma levels was estimated with nitro derivative. Conclusion: The present work recommends that nitration of curcumin improves aqueous solubility which may improve absorption and in vivo bioavailability

Study On Phubbing With The Perspective Of Connection To Disconnection Using Smartphone On Youth Generation

Phubbing has become a topic of interest for researchers worldwide. The rationale is that since cellphones are used in co-present interactions, they are easily accessible. This is known as "phubbing" and is not acceptable behavior in most places. Phubbing is the practice of ignoring people in social situations by looking at one's phone instead of interacting with them, according to Chotpitayasunondh and Douglas (2018). The purpose of this article to provide an overview of phubbing research investigations

Potential of optimized NOvA for large theta(13) & combined performance with a LArTPC & T2K

NOvA experiment has reoptimized its event selection criteria in light of the recently measured moderately large value of theta(13). We study the improvement in the sensitivity to the neutrino mass hierarchy and to leptonic CP violation due to these new features. For favourable values of deltacp, NOvA sensitivity to mass hierarchy and leptonic CP violation is increased by 20%. Addition of 5 years of neutrino data from T2K to NOvA more than doubles the range of deltacp for which the leptonic CP violation can be discovered, compared to stand alone NOvA. But for unfavourable values of deltacp, the combination of NOvA and T2K are not enough to provide even a 90% C.L. hint of hierarchy discovery. Therefore, we further explore the improvement in the hierarchy and CP violation sensitivities due to the addition of a 10 kt liquid argon detector placed close to NOvA site. The capabilities of such a detector are equivalent to those of NOvA in all respects. We find that combined data from 10 kt liquid argon detector (3 years of nu + 3 years of nubar run), NOvA (6 years of nu + 6 years of nubar run) and T2K (5 years of nu run) can give a close to 2 sigma hint of hierarchy discovery for all values of deltacp. With this combined data, we can achieve CP violation discovery at 95% C.L. for roughly 60% values of deltacp.Comment: 22 pages, 24 pdf figures, 5 tables. In the appendix, new results are presented with conservative choices of central values of oscillation parameters. New references are added. Accepted in JHE