Similarity Coefficient Values Research Articles

AI-Assisted Detection and Localization of Spinal Metastatic Lesions

Objectives: The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Methods: Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. Results: The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. Conclusions: The study concludes that AI-based models can augment radiologists’ capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

Automated dentition segmentation: 3D UNet-based approach with MIScnn framework

Advancements in technology have led to the adoption of digital workflows in dentistry, which require the segmentation of regions of interest from cone-beam computed tomography (CBCT) scans. These segmentations assist in diagnosis, treatment planning, and research. However, manual segmentation is an expensive and labor-intensive process. Therefore, automated methods, such as convolutional neural networks (CNNs), provide a more efficient way to generate segmentations from CBCT scans. A three-dimensional UNet-based CNN model, utilizing the Medical Image Segmentation CNN framework, was used for training and generating predictions from CBCT scans. A dataset of 351 CBCT scans, with ground-truth labels created through manual segmentation using AI-assisted segmentation software, was prepared. Data preprocessing, augmentation, and model training were performed, and the performance of the proposed CNN model was analyzed. The CNN model achieved high accuracy in segmenting maxillary and mandibular teeth from CBCT scans, with average Dice Similarity Coefficient values of 91.83% and 91.35% for maxillary and mandibular teeth, respectively. Performance metrics, including Intersection over Union, precision, and recall, further confirmed the model's effectiveness. The study demonstrates the efficacy of the three-dimensional UNet-based CNN model within the Medical Image Segmentation CNN framework for automated segmentation of maxillary and mandibular dentition from CBCT scans. Automated segmentation using CNNs has the potential to deliver accurate and efficient results, offering a significant advantage over traditional segmentation methods.

Morphological and molecular diversity analysis vis-a-vis phylogenetic correlation among pomegranate (Punica granatum L.) varieties

Abstract Identification of different pomegranate (Punica granatum L.) genotypes using morphological descriptors and further substantiated through molecular means is essential for a breeding program. Characterization using molecular markers has an advantage over its morphological counterpart due to its low expense, quick and reliable results, and the requirement of less sophisticated equipment. In this study, eight different pomegranate cultivars were characterized based on their morphological and molecular characters in sub-tropical regions between 2018 and 2019. Morphological experiments were conducted in a randomized block design replicated thrice for each cultivar, whereas RAPD markers were employed for molecular studies. Principal component analysis revealed significant morphological variation (79.61%) among the cultivars. A high level of polymorphism, similar to PCA, was observed with RAPD analysis (80.49%). Some discrepancies between morphological and molecular results were noted in UPGMA cluster analysis. However, eight primers (total = 41) distinctively separated the Bhagwa variety from others, with the maximum similarity coefficient value (ca. 0.54) and superior fruit quality distinguished in PCA analysis with the maximum mean Euclidean distance (ca. 16). Hence, RAPD markers could be used along with morphological markers for a quick screening of germplasms in further crop improvement programs.

Genetic Diversity of Local Chili (Capsicum annum L.) from West Sumatra Indonesia Based on Inter Simple Sequences Repeat (ISSR) Markers

Capsicum annum L. is the most widely cultivated species due to its economic importance and health benefits. West Sumatra is one of the largest chili pepper producers in Indonesia, so it has a lot of local chili genetic resources. There is inadequate information on the genetic diversity of local chilies in West Sumatra. The purpose of this research was to analyze the genetic diversity of 23 genotypes of chili pepper from West Sumatra amplified with 10 markers of ISSR. The material used was leaves from 23 genotypes of West Sumatra’s local chili which are collections of the Agriculture Faculty, Andalas University, Padang, West Sumatra. The results showed that ISSR markers (ISSR primer 12, ISSR UBC, and ISSR 3M) had produced 84 polymorphic DNA bands with a total polymorphic percentage of 61.45%. The dendrogram based on 10 ISSR markers showed that the 23 chili genotypes were retrieved into three main groups (I, II, III, IV) with genetic similarity coefficient values ranging from 0.67 to 0.97 or a diversity of features of 0.03–0.33 (30%). Group I consisted of 1 genotype, group II consisted of 3 genotypes, and group III consisted of 14 genotypes. Kopay chili (G20) has a large genetic distance compared to the other genotypes, while Lolai and Gero chilies have a very high similarity. This genetic diversity information can be used as a basis for generating crosses between genotypes in a breeding program. The farther the genetic distance will produce a high population diversity, the greater the chance of getting superior hybrids. Keywords: Capsicum annum, ISSR, local chili, West Sumatra

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.

To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis. Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software. Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1-4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different. The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.

Reverse Encoding Distortion Correction for Clinical Head Echo-Planar Diffusion-Weighted MRI: Initial Experience.

This study aimed to evaluate the feasibility of the recently commercialized reverse encoding distortion correction (RDC) method for echo-planar imaging (EPI) diffusion-weighted imaging (DWI) by applying clinical head MRI. This study included 50 consecutive patients who underwent head MRI, including single-shot (SS) EPI DWI and RDC-EPI DWI. For evaluation of normal structures, qualitative scores for image distortion, Dice similarity coefficient (DSC) values, distortion ratios, and mean apparent diffusion coefficient (ADC) values were assessed in the pons, temporal lobe at the skull base, and frontal lobe at the level of the lateral ventricles in 30 patients. To evaluate pathologies, qualitative scores for image distortion were assessed for 25 intracranial and 21 extracranial pathologies identified in 32 patients. Qualitative scores for image distortion, DSC values, distortion ratios, and mean ADC values of the pons and temporal lobe were significantly different between SS-EPI DWI and RDC-EPI DWI, whereas those of the frontal lobe at the level of the lateral ventricles were not significantly different between the 2 DWIs. The qualitative scores for image distortion and mean ADC values of extracranial pathologies were significantly different between the DWIs, whereas those of intracranial pathologies were not significantly different. RDC-EPI DWI significantly reduced image distortion and showed higher mean ADC values of the brain parenchyma in the skull base and extracranial pathologies.

Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using Deep Learning.

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling.

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study

PurposeTo evaluate nnU-net’s performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.MethodsWe collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin’s concordance correlation coefficient (CCC).ResultsA total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80–0.82, PPV of 84.5–86.1%, and sensitivity of 77.6–81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland–Altman plots revealed minor tumor volume differences with 0.22–1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively.ConclusionThe nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images.

EEMSNet: Eagle-Eye Multi-Scale Supervised Network for cardiac segmentation

Cardiac segmentation plays a crucial role in computer-aided diagnosis of cardiac diseases. Nevertheless, the task of cardiac segmentation is inherently arduous due to the complex cardiac anatomy, and the intensity heterogeneity introduced during the acquisition of magnetic resonance images. To address these challenges, this paper presents an Eagle-Eye Multi-Scale Supervision Network. The network improves on the Full Convolutional Transformer network and introduces an innovative Eagle-Eye Multi-Scale Supervised module, which is used exclusively for edge supervision during the training process. The edge-supervised branch supervises the structure of cardiac edges at different scales within the input image pyramid structure. During training, the Eagle-Eye Multi-Scale Supervised module is based on multi-scale inputs. It uses edge information from Ground Truth to guide the model in learning the structural and detailed features of cardiac edges, ensuring the model captures edges accurately. Furthermore, considering that most cardiac segmentation networks typically overlook boundary profile information, our designed loss function places particular emphasis on the preservation of boundary profiles after the fusion of multi-scale edge features. Experimental results demonstrate that our proposed the Eagle-Eye Multi-Scale Supervision Network exhibits outstanding performance on the Automatic Cardiac Diagnosis Challenge dataset, achieving a notable average Dice Similarity Coefficient of 93.21%. Particularly, the Dice Similarity Coefficient value for right ventricle segmentation stands out at an impressive 93.52%. These findings signify the exceptional accuracy and effectiveness of our approach in addressing cardiac image segmentation tasks.

MRI Brain Tumor Segmentation using Cuckoo-based Dimensionality Reduction and Ensemble Convolutional Neural Network

Background Brain tumor identification at an early stage is a challenging task that increases the lifetime of patients. Specialists' conclusions on recognizing brain tumors are difficult, as they are based on their theoretical knowledge. It takes a huge amount of time to diagnose the patient. Recently, research has suggested an automated technique that is dependent on convolutional neural networks. Medical pictures are a set of accumulations of data that are hard to store and process, expending broad registering time. The decreased infiltrated systems are normally utilized as an information pre-preparing venture to make the picture information less mind-boggling with the goal that high-dimensional information may be recognized by a fitting and apt low-dimensional portrayal. Objective This study proposes an optimization-based dimensionality reduction and brain tumor segmentation using ensemble convolutional neural networks in MRI images to enhance disease diagnosis and extend healthcare accessibility. Methods Cuckoo-based dimensionality reduction and Ensemble CNN are proposed to segment the tumor region . The cuckoo-based optimization search technique is used to reduce the dimensionality of MRI Brain Images to perform better segmentation. The proposed technique is evaluated on the BRATS database, which contains two datasets: the Leaderboard and Challenge datasets. The outcomes are estimated utilizing the Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity. Results The Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset. The proposed method outperformed the leaderboard dataset with a greater 91% Dice Similarity Coefficient (DCE), 95% Positive Predictive Value, and 87% Sensitivity of High-Grade Glioma (HGG). Seventy-two percent Dice Similarity Coefficient (DCE), 70% Positive Predictive Value, and 93% Sensitivity of Low-Grade Glioma (LGG). 88% Dice Similarity Coefficient (DCE), 90% Positive Predictive Value, and 91% Sensitivity of combined High-Grade glioma and Low-Grade glioma. For the BRATS Challenge dataset, the proposed method provides a 92% Dice Similarity Coefficient (DCE), 93% Positive Predictive Value, and 95% Sensitivity of High-Grade Glioma (HGG). 86% Dice Similarity Coefficient (DCE), 88% Positive Predictive Value and 93% Sensitivity of Low-Grade glioma (LGG). 85% Dice Similarity Coefficient (DCE), 89% Positive Predictive Value, and 92% Sensitivity of combined High-Grade glioma and Low-Grade glioma. Conclusion In this study, MRI Brain tumor segmentation using Cuckoo-based dimensionality reduction and Ensemble Convolutional Neural Network is proposed. The cuckoo search algorithm used for dimensionality reduction is performed in MRI images to reduce the dimensions. We also compared two of the existing methods with our proposed method. The leaderboard dataset and challenge dataset have been discussed. The challenge dataset for HGG provided good results in terms of dice similarity coefficient and positive predictive value. The sensitivity alone gets reduced when compared with the CNN and random forest methods. Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset.

Hubungan Kekerabatan Tanaman Talas (Colocasia esculenta (L.) Schott) di Kabupaten Bengkayang Berdasarkan Karakter Morfologi

Taro (Colocasia esculenta (L.) Scott) is a plant classified as a tuber derived from the genus Colocasia and belongs to the Araceae tribe. Analysis of kinship relationships can be done by observing morphological characters. The purpose of this study was to determine the relationship between taro plants in Bengkayang Regency based on morphological characters. This research was conducted from April to June 2023. Taking 30 samples in 6 sub-districts using purposive sampling method. Analysis of kinship relationships using the NTSys program with the UPGMA method. The results showed that 30 taro accessions had a similarity coefficient value of 0.52-0.93 or 52-93%. The results of kinship relationship analysis obtained 4 clusters at a similarity coefficient value of 0.60 or 60%. The results of morphological characterization based on qualitative and quantitative characters show that taro accessions have diversity in leaf and cormus characters of taro, especially in color characters.

Diversity and genome classification of Banana (Musa spp.) at Kuantan Singingi District - Riau Province, Indonesia

Abstract The objective of this research was to determine the genome group, and to observe the genetic relationship between banana cultivars in the Kuantan Singingi District, Riau Province. Twenty-five local banana cultivars were observed and characterized morphologically by following of IPGRI standard, genome classification was determined according to the Simmond and Shepherd method, and UPGMA clustering were conducted. Twenty-five banana cultivars observed is classified into two genome group, namely AA/AAA genomes, and AAB genome. The coefficient similarity value of twenty-five banana cultivars based on morphological markers ranged from 0.23 to 1.00. Several banana cultivars showed the coefficient similarity value of 1.00, indicating that they are similar morphologically but different in naming in the community. UPGMA dendrogram clustered twenty-five banana cultivars into three groups, The first cluster consists of Kepok, Batu, Tanduk, Nangka, Godang, Ambon, Kapas, and Kolek Siam banana. The second cluster included banana cultivars of Abu, Talun, Godok, Pinang, Kapal, Raja, Sawak, and Serai banana. The third cluster consisted of Lidi, Tunjuk, Rotan, 40Hari, Lomak Manih, Somanih, Emas, Ome, and Bantan banana. This study concluded that a minimum of eighteen banana cultivars should be collected for the conservation program, and hybridization can be performed among banana cultivars having low similarity coefficient values.

A Prostate Boundary Localization and Edge Denoising Algorithm

This research aimed at presenting a two-step method for prostate segmentation in TRUS images. The research used a prostate boundary localization and prostate edge denoising approach. The proposed method contribution is the use of the optimized Hodge’s method as the boundary operator and the use of the Bidirectional Exponential moving average to perform edge denoising. The results showed that the proposed method is effective in completing the prostate segmentation task. (1) The prostate region is effectively initialized and localized. (2) The recovery of noise points is accomplished and the segmentation result being consistent with the general shape of the prostate. The experimental results showed that this method can improve the overall segmentation accuracy. The process uses a combination of traditional and unsupervised methods, eliminating the need to rely on large data sets compared to current deep learning methods. The proposed method achieved excellent segmentation accuracy, with the Dice similarity coefficient (DICE) value of 0.9679, an average Intersection over Union (IoU) value of 0.9377, and an average False Positive Rate (FPR) of 0.0399. The results obtained from this study have significant implications for clinical practice. Accurate prostate segmentation is crucial for various applications, including radiation therapy planning, image-guided interventions, and computer-aided diagnosis. The proposed method has the potential to improve these applications by providing more precise and reliable prostate segmentations. However, it is important to acknowledge some limitations of this study. First, the proposed method was evaluated on a limited dataset, which may not fully represent the diversity of prostate images encountered in clinical practice. Further validation on larger datasets is necessary to assess its generalizability. Additionally, the proposed method relied on manual annotations for training, which can introduce inter-observer variability. Incorporating automated or semi-automated annotation techniques could enhance the robustness of the method

Keragaman Pepaya (Carica papaya L.) di Kotamadya Pontianak Berdasarkan Karakter Morfologi

Papaya (Carica papaya L.) is a plant that is widely cultivated in Indonesia. Pontianak City is one of the centers for the production of papaya cultivation, with a production of 12,593 tons in 2019. This study aims to determine the diversity and kinship of papaya in Pontianak City based on its morphological characters. The research was conducted from September to October 2021 in Pontianak City. A total of 32 individuals were sampled from six districts using the cruising method. Consanguinity was analyzed on 28 morphological characters using the UPGMA method with the NTSYS ver. 2.0. The results indicated that the 32 papaya accessions exhibited a similarity coefficient value of 0.35, which represents a 35% level of similarity. The papaya plants in Pontianak City exhibited a low level of similarity but a high level of diversity.

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images

The imbalance of land cover categories is a common problem. Some categories appear less frequently in the image, while others may occupy the vast majority of the proportion. This imbalance can lead the classifier to tend to predict categories with higher frequency of occurrence, while the recognition effect on minority categories is poor. In view of the difficulty of land cover remote sensing image multi-target semantic classification, a semantic classification method of land cover remote sensing image based on depth deconvolution neural network is proposed. In this method, the land cover remote sensing image semantic segmentation algorithm based on depth deconvolution neural network is used to segment the land cover remote sensing image with multi-target semantic segmentation; Four semantic features of color, texture, shape and size in land cover remote sensing image are extracted by using the semantic feature extraction method of remote sensing image based on improved sequential clustering algorithm; The classification and recognition method of remote sensing image semantic features based on random forest algorithm is adopted to classify and identify four semantic feature types of land cover remote sensing image, and realize the semantic classification of land cover remote sensing image. The experimental results show that after this method classifies the multi-target semantic types of land cover remote sensing images, the average values of Dice similarity coefficient and Hausdorff distance are 0.9877 and 0.9911 respectively, which can accurately classify the multi-target semantic types of land cover remote sensing images.

Homogeneity pursuit and variable selection in regression models for multivariate abundance data.

When building regression models for multivariate abundance data in ecology, it is important to allow for the fact that the species are correlated with each other. Moreover, there is often evidence species exhibit some degree of homogeneity in their responses to each environmental predictor, and that most species are informed by only a subset of predictors. We propose a generalized estimating equation(GEE) approach for simultaneous homogeneity pursuit (ie, grouping species with similar coefficient values while allowing differing groups for different covariates) and variable selection in regression models for multivariate abundance data. Using GEEs allows us to straightforwardly account for between-response correlations through a (reduced-rank) working correlation matrix. We augment the GEE with both adaptive fused lasso- and adaptive lasso-type penalties, which aim to cluster the species-specific coefficients within each covariate and encourage differing levels of sparsity across the covariates, respectively. Numerical studies demonstrate the strong finite sample performance of the proposed method relative to several existing approaches for modeling multivariate abundance data. Applying the proposed method to presence-absence records collected along the Great Barrier Reef in Australia reveals both a substantial degree of homogeneity and sparsity in species-environmental relationships. We show this leads to a more parsimonious model for understanding the environmental drivers of seabed biodiversity, and results in stronger out-of-sample predictive performance relative to methods that do not accommodate such features.

GENETIC CHARACTERIZATION AND POPULATION STRUCTURE BY iPBS MARKERS OF BOTTLE GOURD (Lagenaria siceraria) GENOTYPES

Bottle gourd (Lagenaria siceraria (Molina) Standl.) is a significant cucurbit plant species whose ripe fruits are consumed as ornamental items and immature fruits are consumed as vegetables. In addition, these plants are used as rootstocks in vegetable grafts due to their strong root structure. This study aimed to determine the genetic diversity of bottle gourd genotypes whose fresh fruits are consumed. Using iPBS (Inter-Primer Binding Site) retrotransposon marker techniques, a total of 186 bands in 30 genotypes were obtained and the polymorphism rate was calculated as 93.3%. Similarity coefficient values were found in the range of 0.35-0.96. Three main clusters emerged from cluster analysis. As a result of the structure analysis, genotypes were divided into four subpopulations. There was also a high level of genetic diversity among bottle gourd genotypes. To improve a variety's traits, genetic diversity can be used. The iPBS technique is also a powerful tool for understanding the genetic characterization of bottle gourd. These data can be used in breeding strategies to improve bottle gourd varieties. Keywords: Bottle Gourd, iPBS, Laneraria siceraria, molecular characterization, vegetable

Deep learning methods for early detection of Alzheimer’s disease using structural MR images: a survey

In this paper, we present an extensive review of the most recent works on Alzheimer’s disease (AD) prediction, focusing on Moderate Cognitive Impairment (MCI) conversion prediction. We aimed to identify the most useful brain-magnetic resonance imaging (MRI) biomarkers and deep learning frameworks used for prediction. To achieve this, we analyzed more than 130 studies and reviewed 7 articles. A closer examination revealed that the hippocampus is an important region of interest (ROI) affected early by AD, and many related features help detect the disease in its early stages. However, when considered alone, this ROI is not sufficient to ensure high prediction performance. Therefore, several other brain regions can also provide additional information to improve prediction accuracy. Concerning state-of-the-art deep neural networks, the U-Net represents the most efficient architecture for hippocampus segmentation. The RESU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) value, equal to 94%.For MCI conversion prediction, the best results were obtained by two models identifying significant landmarks from the entire brain for classification. The multi-stream convolutional neural network achieved the best Area Under the Curve (AUC) and specificity of 94.39% and 99.70%, respectively. Finally, a region ensemble model delivered the highest accuracy of 85.90%, highlighting the need for further research to address this challenging problem.

Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7MHz wide field swept source FDML optical coherence tomography.

The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid. We present a single segmentation framework technique for segmenting the entire layers present in the chorio-retinal complex of the human eye using optical coherence tomography (OCT) images. This automatic program is based on the graph theory method. This single program is capable of segmenting seven layers of the retina and choroid scleral interface. The graph theory was utilized to find the probability matrix and subsequent boundaries of different layers. The program was also implemented to segment angiographic maps of different chorio-retinal layers using "segmentation matrices." The method was tested and successfully validated on OCT images from six normal human eyes as well as eyes with non-exudative age-related macular degeneration (AMD). The thickness of microstructure and microvasculature for different layers located in the chorio-retinal segment of the eye was also generated and compared. A decent efficiency in terms of processing time, sensitivity, and accuracy was observed compared to the manual segmentation and other existing methods. The proposed method automatically segments whole OCT images of chorio-retinal complex with augmented probability maps generation in OCT volume dataset. We have also evaluated the segmentation results using quantitative metrics such as Dice coefficient and Hausdorff distance This method realizes a mean descent Dice similarity coefficient (DSC) value of 0.82 (range, 0.816-0.864) for RPE and CSI layer.