Search results for: hepatic lesion segmentation

Authors: Nor Hidayah Reduwan, Jira Chindasombatjaroen, Suchaya Pornprasersuk-Damrongsri, Sopee Pomsawat

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INTRODUCTION: Odontogenic keratocyst (OKC) remain as a controversial pathologic entity under the scrutiny of many researchers and maxillofacial surgeons alike. The high recurrence rate and relatively aggressive nature of this lesion demand a meticulous analysis of the radiographic characteristic of OKC leading to the formulation of an accurate diagnosis. OBJECTIVE: This study aims to determine the radiographic characteristic of odontogenic keratocyst (OKC) using conventional radiographs and cone beam computed tomography (CBCT) images. MATERIALS AND METHODS: Patients histopathologically diagnosed as OKC from 2003 to 2016 by Oral and Maxillofacial Pathology Department were retrospectively reviewed. Radiographs of these cases from the archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry Mahidol University were retrieved. Assessment of the location, shape, border, cortication, locularity, the relationship of lesion to embedded tooth, displacement of adjacent tooth, root resorption and bony expansion of the lesion were conducted. RESULTS: Radiographs of 91 patients (44 males, 47 females) with the mean age of 31 years old (10 to 84 years) were analyzed. Among all patients, 5 cases were syndromic patients. Hence, a total of 103 OKCs were studied. The most common location was at the ramus of mandible (32%) followed by posterior maxilla (29%). Most cases presented as a well-defined unilocular radiolucency with smooth and corticated border. The lesion was in associated with embedded tooth in 48 lesions (47%). Eighty five percent of embedded tooth are impacted 3rd molar. Thirty-seven percentage of embedded tooth were entirely encapsulated in the lesion. The lesion attached to the embedded tooth at the cementoenamel junction (CEJ) in 40% and extended to part of root in 23% of cases. Teeth displacement and root resorption were found in 29% and 6% of cases, respectively. Bony expansion in bucco-lingual dimension was seen in 63% of cases. CONCLUSION: OKCs were predominant in the posterior region of the mandible with radiographic features of a well-defined, unilocular radiolucency with smooth and corticated margin. The lesions might relate to an embedded tooth by surrounding an entire tooth, attached to the CEJ level or extending to part of root. Bony expansion could be found but teeth displacement and root resorption were not common. These features might help in giving the differential diagnosis.

Keywords: cone beam computed tomography, imaging dentistry, odontogenic keratocyst, radiographic features

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Authors: Ya'u Muhammad, Umar Umar A. Mallammadori, Dahiru Mansur

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Effect of kaempferol on haematological parameters in two weeks old broiler chickens with experimental Eimeria tenella infection was evaluated in this study. Sixty-day old broilers were randomly allotted into six groups (I-VI) of ten broilers each and brooded for two weeks with commercial broiler feed (vital feed®) and provided water ad libitum. At two weeks of age broilers in group 1 were neither infected nor treated. Broilers in groups II-VI were infected with Eimeria tenella sporulated oocyst (104/ml) via oral inoculation. After infection was established, broilers in groups II-IV were treated orally with 1 mg/kg, 1.5 mg/kg, and 2 mg/kg of kaempferol, respectively. Broilers in group V were treated for five days with amprolium, 1.25 g/L in drinking water. Broilers in group VI were administered normal saline, 5 ml/kg per os for five days. Five days post infection; all broilers were sacrificed by severing their jugular veins. Blood sample from each bird was collected in EDTA container for haematology. Caecal contents were harvested and used to determine the lesion score and caecal Oocyst count respectively. Data obtained was analyzed using pad prism version 5.0. Mean Packed Cell Volume (PCV), haemoglobin (Hb) concentration, and Red Blood Cell (RBC) count significantly (P < 0.05) increased in groups II, III, and IV in a dose dependent manner. Similarly, PCV, Hb concentration, and RBC count significantly (P < 0.05) increased in groups II, III, and IV when compared to VI. No significant (P > 0.05) difference in the mean values of PCV, Hb and RBC count were recorded between groups treated with kaempferol and group V. Caecal Oocyst counts and lesion scores reduced significantly (P < 0.05) in groups II, III, and IV in a dose dependent manner. It was therefore observed in this study that kaempferol improved haematological parameters and reduced Oocyst count as well as the lesion scores in broilers infected with Eimeria tenella.

Keywords: broilers, Eimeria tenella, kaempferol, lesion scores, oocyst count,

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Authors: Shafiullah Soomro

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Active contour is one of the image segmentation techniques and its goal is to capture required object boundaries within an image. In this paper, we propose a novel image segmentation method by using an active contour method based on anisotropic diffusion feature enhancement technique. The traditional active contour methods use only pixel information to perform segmentation, which produces inaccurate results when an image has some noise or complex background. We use Perona and Malik diffusion scheme for feature enhancement, which sharpens the object boundaries and blurs the background variations. Our main contribution is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. By minimizing an energy function using partial differential framework the proposed method captures semantically meaningful boundaries instead of catching uninterested regions. Finally, we use a Gaussian kernel which eliminates the problem of reinitialization in level set function. We use several synthetic and real images from different modalities to validate the performance of the proposed method. In the experimental section, we have found the proposed method performance is better qualitatively and quantitatively and yield results with higher accuracy compared to other state-of-the-art methods.

Keywords: active contours, anisotropic diffusion, level-set, partial differential equations

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Authors: Amr A. Fouad, Waleed H. Albuali, Iyad Jresat

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The protective effect of hesperidin was investigated in rats exposed to liver injury induced by a single intraperitoneal injection of cyclophosphamide (CYP) at a dose of 150 mg kg-1. Hesperidin treatment (100 mg kg-1/day, orally) was applied for seven days, starting five days before CYP administration. Hesperidin significantly decreased the CYP-induced elevations of serum alanine aminotransferase, and hepatic malondialdehyde and myeloperoxidase activity, significantly prevented the depletion of hepatic glutathione peroxidase activity resulted from CYP administration. Also, hesperidin ameliorated the CYP-induced liver tissue injury observed by histopathological examination. In addition, hesperidin decreased the CYP-induced expression of inducible nitric oxide synthase, tumor necrosis factor-α, cyclooxygenase-2, Fas ligand, and caspase-9 in liver tissue. It was concluded that hesperidin may represent a potential candidate to protect against CYP-induced hepatotoxicity.

Keywords: hesperidin, cyclophosphamide, liver, rats

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: A. N.Ukwuani, A. K. Abdulfatah

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G. crenata is used locally for the treatment of fractured bones, wound healing and inflammatory conditions. In vitro and in vivo antioxidant activity of hydromethanolic extracts of the leaves of G. crenata were assessed. The phytochemical analysis shows the presence of phenols, flavonoids, saponins, cardiac glycosides and tannins. An in vitro quantitative analysis of phenols, flavonoids and tannins respectively were (164±1.20, 199±0.88 and 88.67±0.88 mg/100g FW). In vivo studies of hydromethanolic extract demonstrated a dose dependent increase in hepatic superoxide dismutase (1.14±0.14, 2.13±0.11, 2.55±0.11 U/mg Protein) with improvement in hepatic glutathione (6.98±0.42, 8.91±0.37, 11.07±0.46 µM/mg Protein) and Catalase (4.47±0.05, 6.24±0.02, 7.17±0.04 U/mg Protein) and Total protein (6.18±0.08, 6.69±0.18, 7.27±0.16 mg/ml) respectively at 100-300mg/kg body weight Grewia crenata leaves when compared to the control and standard drug. It can be concluded from the present findings of that G. crenata leaves possess antioxidant potential.

Keywords: Grewia crenata, antioxidant, hydromethanolic extract, in vivo, in vitro

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Authors: Kaihong Yu, Tetsui Yamashita, Shigeaki Shingyochi, Kazuo Matsumoto, Makoto Ohta

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During cardiac ablation, high power delivery for deeper lesion formation is limited by electrode-tissue interface overheating which can cause serious complications such as thrombus. To prevent this overheating, temperature control and open irrigation are often used. In temperature control, radiofrequency generator is adjusted to deliver the maximum output power, which maintains the electrode temperature at a target temperature (commonly 55°C or 60°C). Then the electrode-tissue interface temperature is also limited. The electrode temperature is a result of heating from the contacted tissue and cooling from the surrounding blood. Because the cooling from blood is decreased under conditions of low blood flow, the generator needs to decrease the output power. Thus, temperature control cannot deliver high power under conditions of low blood flow. In open irrigation, saline in room temperature is flushed through the holes arranged in the electrode. The electrode-tissue interface is cooled by the sufficient environmental cooling. And high power delivery can also be done under conditions of low blood flow. However, a large amount of saline infusions (approximately 1500 ml) during irrigation can cause other serious complication. When open irrigation cannot be used under conditions of low blood flow, a new overheating prevention may be required. The authors have proposed a new electrode cooling method by making the catheter vibrating. The previous work has introduced that the vibration can make a cooling effect on electrode, which may result form that the vibration could increase the flow velocity around the catheter. The previous work has also proved that increasing vibration frequency can increase the cooling by vibration. However, the effect of the vibration amplitude is still unknown. Thus, the present study investigated the effect of vibration amplitude on tissue temperature and lesion size. An agar phantom model was used as a tissue-equivalent material for measuring tissue temperature. Thermocouples were inserted into the agar to measure the internal temperature. Porcine myocardium was used for lesion size measurement. A normal ablation catheter was set perpendicular to the tissue (agar or porcine myocardium) with 10 gf contact force in 37°C saline without flow. Vibration amplitude of ± 0.5, ± 0.75, and ± 1.0 mm with a constant frequency (31 Hz or 63) was used. A temperature control protocol (45°C for agar phantom, 60°C for porcine myocardium) was used for the radiofrequency applications. The larger amplitude shows the larger lesion sizes. And the higher tissue temperatures in agar phantom are also shown with the higher amplitude. With a same frequency, the larger amplitude has the higher vibrating speed. And the higher vibrating speed will increase the flow velocity around the electrode more, which leads to a larger electrode temperature decrease. To maintain the electrode at the target temperature, ablator has to increase the output power. With the higher output power in the same duration, the released energy also increases. Consequently, the tissue temperature will be increased and lead to larger lesion sizes.

Keywords: cardiac ablation, electrode cooling, lesion size, tissue temperature

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Authors: Pavel Chmelar, Martin Dobrovolny

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Laser projection or laser footprint detection is today widely used in many fields of robotics, measurement, or electronics. The system accuracy strictly depends on precise laser footprint detection on target objects. This article deals with the laser line detection based on the RGB segmentation and the component labeling. As a measurement device was used the developed optical rangefinder. The optical rangefinder is equipped with vertical sweeping of the laser beam and high quality camera. This system was developed mainly for automatic exploration and mapping of unknown spaces. In the first section is presented a new detection algorithm. In the second section are presented measurements results. The measurements were performed in variable light conditions in interiors. The last part of the article present achieved results and their differences between day and night measurements.

Keywords: color segmentation, component labelling, laser line detection, automatic mapping, distance measurement, vector map

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Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

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In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Surita Maini

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There are many perceived advantages of microwave ablation have driven researchers to develop innovative antennas to effectively treat deep-seated, non-resectable hepatic tumors. In this paper a coaxial antenna with a miniaturized sleeve choke has been discussed for microwave interstitial ablation therapy, in order to reduce backward heating effects irrespective of the insertion depth into the tissue. Two dimensional Finite Element Method (FEM) is used to simulate and measure the results of miniaturized sleeve choke antenna. This paper emphasizes the importance of factors that can affect simulation accuracy, which include mesh resolution, surface heating and reflection coefficient. Quarter wavelength choke effectiveness has been discussed by comparing it with the unchoked antenna with same dimensions.

Keywords: microwave ablation, tumor, finite element method, coaxial slot antenna, coaxial dipole antenna

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: Saleh N. Maodaa

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Perinatal exposure to nicotine imbalances the redox status in newborns. This study investigated the effect of Anethum graveolens (dill) extract on oxidative stress and tissue injury in the liver and kidney of mice newborns exposed to nicotine perinatally. Pregnant mice received nicotine (0.25 mg/kg) on gestational day 12 to day 5 after birth and/or A. graveolens extract on a gestational day 1 to day 15 after birth. Newborn mice exposed to nicotine showed multiple histopathological alterations in the kidney and liver, including inflammatory cell infiltration and degenerative changes. Nicotine exposure increased hepatic and renal reactive oxygen species (ROS), lipid peroxidation, tumor necrosis factor (TNF-_), interleukin-6 (IL-6), and inducible nitric oxide synthase (iNOS) (p < 0.001), and decreased antioxidant defenses (p < 0.001). A. graveolens supplementation significantly prevented liver and kidney injury, suppressed ROS generation (p < 0.001), lipid peroxidation (p < 0.001), and inflammatory response (p < 0.001), and enhanced antioxidant defenses. In addition, A. graveolens upregulated hepatic and renal Nrf2 and HO-1 mRNA and increased HO-1 activity in normal and nicotine-exposed mice. In conclusion, A. graveolens protects against perinatal nicotine-induced oxidative stress, inflammation, and tissue injury in the liver and kidney of newborn mice. A. graveolens upregulated hepatic and renal Nrf2/HO-1 signaling and enhanced antioxidant defenses in mice.

Keywords: dill, oxidative stress, cytokines, nicotine

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Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

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Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

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Authors: Rojith K. Balakrishnan

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Plasmablastic lymphoma (PBL) is an uncommon, recently described B-cell derived lymphoma that is most commonly seen in patients with Human Immunodeficiency Virus (HIV) infection. Here we report a case of PBL in a 35 year old man with HIV who presented with multiple subcutaneous swellings all over the body and oral mucosal lesions.The biopsy report was suggestive of Diffuse Large B Cell Lymphoma. Immunohistochemistry was done which showed, lymphoma cells, positive for MUM1, CD 138, and VS 38. The proliferation index (MIB) was 95%. Final report was consistent with the diagnosis of Plasmablastic Lymphoma. The lesion completely regressed after treatment with systemic chemotherapy. Up to date, only a few cases of plasmablastic lymphoma have been reported from India. Increased frequency of this lymphoma in HIV patients and rarity of the tumour, along with rapid response of the same to chemotherapy, make this case a unique one. Hence the knowledge about this new entity is important for clinicians who deal with HIV patients.

Keywords: human immunodeficiency virus (HIV), oral cavity lesion, plasmablastic lymphoma, subcutaneous swelling

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Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

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Authors: Plessen Mogens Graf

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Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

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Authors: So Young Kim, Eun-Young Kwon, Bora Choi, Mi Kyeong Yu, Seon Jeong Lee, Myung-Sook Choi

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Lemon (Citrus limon) has various beneficial effect. Eriocitrin (eriodictyol 7-rutinoside) is the main ingredient of lemon fruit and is known to have antioxidative effects. However, there has been little research about the effects of eriocitrin on obesity and regulation of lipid profiles levels. In the present study, we investigated the anti-obesity and lipid-lowering effects of eriocitrin in mice fed high-fat diet (HFD). The 4 week-old male C57BL/6 mice were randomly divided into two groups and were fed HFD (20% fat, w/w) and HFD supplemented with eriocitrin (0.005%, w/w, EC) for 16 weeks. Food intake, body weight and white adipose tissue weight (WAT) were measured and plasma free fatty acid (FFA), apolipoprotein (Apo) B100 level and hepatic enzyme activity were analyzed. No differences were shown between the HFD and EC groups in body weight and food intake. However EC supplementation significantly reduced the weights of epididymal, subcutaneous and total WAT. In addition, the levels of plasma FFA and Apo B100 were significantly decreased in the EC group compared with the HFD group. Moreover, the activities of glucose-6-phosphate dehydrogenase (G6PD) and malic enzyme (ME) related to fatty acids synthesis were significantly lower in the EC group than in the HFD group in liver. Therefore, this study indicates that eriocitrin has beneficial effects on adiposity and nonalcholic fatty liver diseases by modulating hepatic lipid-regulating enzyme activities and plasma lipid profile.

Keywords: antiobesity, eriocitrin, high fat diet, lipid lowering

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Authors: Parisa Mansour

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The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Bing-Fang Lee, Srinivasan Periasamy, Ming-Yie Liu

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Shift work sleep disorder is a common problem in industrialized world. It is a type of circadian rhythmic sleep disorders characterized by insomnia and sleep deprivation. Lack of sleep in workers may lead to poor health conditions such as hepatic dysfunction. Melatonin is a hormone secreted by the pineal gland to alleviate insomnia. Moreover, it is a powerful antioxidant and may prevent acute liver injury. Therefore, workers take in melatonin to deal with sleep-related health is an important issue. The aim of this study was to investigate the effect of melatonin on an acute hepatic injury model sinusoidal obstruction syndrome (SOS) in mice. Male C57BL/6 mice were injected with a single dose (500 mg/kg) of monocrotaline (MCT) to induce SOS. Melatonin (1, 3, 10 and 30 mg/kg) was injected 1 h before MCT treatment. After 24 h of MCT treatment, mice were sacrificed. The blood and liver were collected. Organ damage was evaluated by serum biochemistry, hematology analyzer, and histological examination. Low doses of melatonin (1 and 3 mg/kg) had no protective effect on SOS. However, high doses (10 and 30 mg/kg) exacerbated SOS. In addition, it not only increased serum glutamate oxaloacetate transaminase (GOT), glutamate pyruvate transaminase (GPT) and extended liver damage indicated by histological examination but also decreased platelet levels, lymphocyte ratio, and glutathione level; it had no effect on malondialdehyde and nitric oxide level in SOS mice. To conclude, melatonin may exacerbate MCT-induced SOS in mice. Furthermore, melatonin might have a synergistic action with SOS. Usage of melatonin for insomnia by people working in long shift must be cautioned; it might cause acute hepatic injury.

Keywords: acute liver injury, melatonin, shift work, sleep deprivation

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Authors: Deepak Mohan, Sushma Sharma

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Diclofenac sodium, a member of the acetic acid family of non-steroidal anti-inflammatory drugs, is used to retard inflammation, arthritis pain and ankylosing spondylitis. The drug is known to cause severe injury in different tissues due to formation of reactive oxygen species. The present study is focused on the effect of different doses of diclofenac (4 mg/kg/body weight and 14 mg/kg/body weight on histoarchitecture of the liver from 7-28 days of the investigation. Diclofenac administration resulted in distorted hepatic degeneration and formation of wide areas in the form of sinusoidal gaps. Hepatic fibrosis noticed in different stages of investigation could be attributed to chronic inflammation and reactive oxygen species which results in deposition of extracellular matrix proteins. The abrupt degenerative changes observed during later stages of the experiment showed maximum damage to the liver, and there was enlargement of sinusoidal gaps accompanied by maximum necrosis in the tissues.

Keywords: arthritis, diclofenac, histoarchitecture, sinusoidal

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Authors: Namita Mittal, Himakshi Shekhawat, Ankit Vidyarthi

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In medical research study, doctors and radiologists face lot of complexities in analysing the brain tumors in Magnetic Resonance (MR) images. Brain tumor detection is difficult due to amorphous tumor shape and overlapping of similar tissues in nearby region. So, radiologists require one such clinically viable solution which helps in automatic segmentation of tumor inside brain MR image. Initially, segmentation methods were used to detect tumor, by dividing the image into segments but causes loss of information. In this paper, a hybrid method is proposed which detect Region of Interest (ROI) on the basis of difference in intensity values and texture values of tumor region using nearby tissues with Gradient Vector Flow (GVF) technique in the identification of ROI. Proposed approach uses both intensity and texture values for identification of abnormal section of the brain MR images. Experimental results show that proposed method outperforms GVF method without any loss of information.

Keywords: brain tumor, GVF, intensity, MR images, segmentation, texture

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Authors: N. F. Hamid, A. Kipar, J. Stewart, D. J. Antoine, B. K. Park, D. P. Williams

Abstract:

Acetaminophen (APAP) is a widely used analgesic that is safe at therapeutic doses. The mouse model of APAP has been extensively used for studies on pathogenesis and intervention of drug induced liver injury based on the CytP450 mediated formation of N-acetyl-p-benzo-quinoneimine and, more recently, as model for mechanism based biomarkers. Delay of the fasted CD1 mice to rebound to the basal level of hepatic GSH compare to fed mice is reported in this study. Histologically, 15 hours fasted mice prior to APAP treatment leading to overall more intense cell loss with no evidence of apoptosis as compared to non-fasted mice, where the apoptotic cells were clearly seen on cleaved caspase-3 immunostaining. After 15 hours post APAP administration, hepatocytes underwent stage of recovery with evidence of mitotic figures in fed mice and return to completely no histological difference to control at 24 hours. On the contrary, the evidence of ongoing cells damage and inflammatory cells infiltration are still present on fasted mice until the end of the study. To further measure the regenerative capacity of the hepatocytes, the inflammatory mediators of cytokines that involved in the progression or regression of the toxicity like TNF-α and IL-6 in liver and spleen using RT-qPCR were also included. Yet, quantification of proliferating cell nuclear antigen (PCNA) has demonstrated the time for hepatic regenerative in fasted is longer than that to fed mice. Together, these data would probably confirm that fasting prior to APAP treatment does not only modulate liver injury, but could have further effects to delay subsequent regeneration of the hepatocytes.

Keywords: acetaminophen, liver, proliferating cell nuclear antigen, regeneration, apoptosis

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Authors: Amberkar Mohanbabu Vittalrao, Avin, Meena Kumari Kamalkishore, Padmanabha Udupa, Vinaykumar Bavimane, Honnegouda

Abstract:

Objective: To evaluate the hepato-protective activity of Tinospora cordiofolia (Tc) against paracetamol induced hepatic damage in rats. Methods: The plant stem (test drug) was procured locally, shade dried, powdered and extracted with water. Silymarin was used as standard hepatoprotective drugs and 2% gum acacia as a control (vehicle) against paracetamol (PCT) induced hepatotoxicity. Results and Discussion: The hepato-protective activity of aqueous stem extract was assessed by paracetamol induced hepatotoxicity preventive model in rats. Alteration in the levels of biochemical markers of hepatic damage like AST, ALT, ALP and lipid peroxides were tested in both paracetamol treated and untreated groups. Paracetamol (3g/kg) had enhanced the AST, ALT, ALP and the lipid peroxides in the serum. Treatment of silymarin and aqueous stem extract of Tc (200 and 400mg/kg) extract showed significant hepatoprotective activity by altering biochemical marker levels to the near normal. Preliminary phytochemical tests were done. Aqueous Tc extract showed presence of phenolic compound and flavonoids. Our findings suggested that Tc extract possessed hepatoprotective activity in a dose dependent manner. Conclusions: Tc was found to possess significant hepatoprotective property when treated with PCT. This was evident by decreasing the liver enzymes significantly when treated with PCT as compared to PCT only treated group (P < 0.05). Hence Tinospora cardiofolia could be a good, promising, preventive agent against PCT induced hepatotoxicity.

Keywords: Tinospora cardiofolia, hepatoprotection, paracetamol, silymarin

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Authors: Donatella Giuliani

Abstract:

In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: Bouaziz H., Soudania N., Essafia M., Ben Amara I., Hakim A., Jamoussi K., Zeghal Km, Zeghal N.

Abstract:

In this investigation, we have evaluated the effects of arsenic trioxide on hepatic function in pregnant and lactating Swiss albino mice and their suckling pups. Experiments were carried out on female mice given 175 ppm As2O3 in their drinking water from the 14th day of pregnancy until day 14 after delivery. Our results showed a significant decrease in plasma levels of total protein and albumin, cholesterol and triglyceride in As2O3 treated mice and their pups. The hyperbilirubinemia and the increased plasma total alkaline phosphatase activity suggested the presence of cholestasis. Transaminase activities as well as lactate deshydrogenase activity in plasma, known as biomarkers of hepatocellular injury, were elevated indicating hepatic cells’damage after treatment with As2O3. Exposure to arsenic led to an increase of liver thiobarbituric acid reactive substances level along with a concomitant decrease in the activities of superoxide dismutase, catalase and glutathione peroxidase and in glutathione.

Keywords: antioxidant status, arsenic trioxide, hepatotoxicity, mice, oxidative stress

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Authors: Aishik Deb, Rituparna Sinha

Abstract:

We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

Abstract:

This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

Procedia PDF Downloads 490