Similarity Coefficient Values Research Articles

A new scheme for automatic 2D detection of spheric and aspheric femoral heads: A case study on coronal MR images of bilateral hip joints of patients with Legg-Calve-Perthes disease

Background and objectiveIn orthopaedics, hip joint and circular structure of the femoral head can be distorted by a wide variety of hip joint disorders. Hence, automatic detection and segmentation of the femoral head is an important issue in the studies of computerized hip joint segmentation, quantification and assessment. In the proposed study, we need to detect the center coordinates and radius of spheric and aspheric femoral heads automatically in bilateral hip magnetic resonance (MR) images using a new scheme. MethodsThis paper presents a new two-level scheme based on the Circular Hough Transform (CHT) and Integro-differential Operator (IDO) to detect the spheric and aspheric femoral heads in MR images in 2D. Initially, MR slices are divided vertically into two equal halves to automatically separate the hip joints and Canny’s edge detection method is performed on each of the halves to obtain edge images. Then, healthy and pathological femoral heads are detected by performing the CHT over edge images in the first stage of proposed scheme. In the second stage, femoral head circle detected with CHT is fine tuned by performing Daugman’s IDO. ResultsPerformance evaluations of the proposed femoral head detection scheme were carried out on healthy and pathological hip joints in 24 coronal MR image sections belong to 13 patients with Legg-Calve-Perthes Disease (LCPD). In performance evaluations on 24 healthy and 24 pathological hip joints, Root Mean Square Error (RMSE) and Dice Similarity Coefficient (DSC) values were measured for automatically detected femoral heads. We observed 1.96 mm. (std. 1.21 mm.) mean RMSE for center coordinates, 1.45 mm. (std. 1.39 mm) mean RMSE for radii, 0.8978 (std. 0.0733) mean DSC on healthy femoral heads and 3.56 mm. (std. 3.19 mm.) mean RMSE for center coordinates, 1.56 mm. (std. 1.33 mm.) mean RMSE for radii, 0.8529 (std. 0.0927) mean DSC on pathological femoral heads. ConclusionsProposed femoral head detection scheme has promising results for the detection and the segmentation of the spheric and aspheric femoral heads and also has a potential to be used in detection of the other anatomical structures having a circular shape.

Improving Splenomegaly Segmentation by Learning from Heterogeneous Multi-Source Labels.

Splenomegaly segmentation on computed tomography (CT) abdomen anatomical scans is essential for identifying spleen biomarkers and has applications for quantitative assessment in patients with liver and spleen disease. Deep convolutional neural network automated segmentation has shown promising performance for splenomegaly segmentation. However, manual labeling of abdominal structures is resource intensive, so the labeled abdominal imaging data are rare resources despite their essential role in algorithm training. Hence, the number of annotated labels (e.g., spleen only) are typically limited with a single study. However, with the development of data sharing techniques, more and more publicly available labeled cohorts are available from different resources. A key new challenging is to co-learn from the multi-source data, even with different numbers of labeled abdominal organs in each study. Thus, it is appealing to design a co-learning strategy to train a deep network from heterogeneously labeled scans. In this paper, we propose a new deep convolutional neural network (DCNN) based method that integrates heterogeneous multi-resource labeled cohorts for splenomegaly segmentation. To enable the proposed approach, a novel loss function is introduced based on the Dice similarity coefficient to adaptively learn multi-organ information from different resources. Three cohorts were employed in our experiments, the first cohort (98 CT scans) has only splenomegaly labels, while the second training cohort (100 CT scans) has 15 distinct anatomical labels with normal spleens. A separate, independent cohort consisting of 19 splenomegaly CT scans with labeled spleen was used as testing cohort. The proposed method achieved the highest median Dice similarity coefficient value (0.94), which is superior (p-value<0.01 against each other method) to the baselines of multi-atlas segmentation (0.86), SS-Net segmentation with only spleen labels (0.90) and U-Net segmentation with multi-organ training (0.91). Our approach for adapting the loss function and training structure is not specific to the abdominal context and may be beneficial in other situations where datasets with varied label sets are available.

GENETIC DIVERSITY AMONG AND WITHIN BRAVE BEAN (Capparis flexuosa L.) POPULATIONS ASSESSED USING RAPD MARKERS

ABSTRACT Brave bean (Capparis flexuosa L.) is a Caatinga species that is used as forage, mainly during the dry season when some plant species lose their leaves. The aim of this study was to assess genetic diversity within and among brave bean populations using Random Amplified Polymorphic DNA (RAPD) markers. Brave bean leaves were collected from 30 accessions in the following municipalities of Paraíba state, Brazil: Barra de Santa Rosa (BSR), Cuité (C), São João do Cariri (SJC), Damião (D), Baraúna (B), and Picuí (P). DNA extraction followed the standard methodology of CTAB with modifications. RAPD analyses were carried out using 18 primers, and polymorphism of the amplified DNA fragments was visualized using agarose gel electrophoresis. Data were used to calculate Jaccard Similarity Coefficient values, which were then used to group samples with the Unweighted Pair Group Method with Arithmetic Mean. Cophenetic Correlation Coefficient, Stress, and Distortion Coefficient values were also calculated from these analyses. Band polymorphism was generated with 14 primers, but the sampled populations showed low numbers of polymorphic loci (27 in BSR, 18 in C, 7 in SJC, 9 in D, and 0 in B and P). The highest polymorphic information content was found in samples from the BSR (9 groups), C (22 groups), SJC (7 groups), and D (6 groups) municipalities. In the interpopulation analysis, 34 groups were formed, the matrices of which showed high cophenetic correlations (0.95 to 0.98), but low stress (12.9 to 17.45%) and distortion (3.05%). Therefore, results showed that there was genetic variability both among and within brave bean populations.

A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI

This paper presents a novel approach for automatic segmentation of MS lesion including both of number and volume. The novelty includes the combination of the multiplicative intrinsic component optimization algorithm (Li et al. in Magn Reson Imaging 32:913–923, 2014) in bias field correction and normal tissue segmentation simultaneously, and the development of a multi-atlas and multi-channel (MAMC) segmentation approach. The first research focus is the classification of brain tissue into white matter, cerebrospinal fluid and gray matter in T1-w image and FLAIR image. The second research focus is the segmentation of MS lesion in white matter region using atlas. In label fusion, the coefficient as a specific weight is assigned to target label image based on the correlation function between atlases. This novel MAMC approach is evaluated by 20 training cases obtained from Medical Image Computing and Computer Aided Intervention Society 2008 MS Lesions Segmentation Challenge. The numerical results are presented in terms of accuracy, specificity and absolute volume difference. A comparison of MAMC approach and other conventional approaches is presented in terms of the true positive rate and the positive predictive value. Furthermore, the total lesion volume is calculated and compared with expert delineation. It can be seen that the MAMC approach is able to acquire a larger mean value of the Dice similarity coefficient than the other conventional approaches do. Therefore, this novel approach is an added value for the clinical evaluation of MS patients.

Morphological and Molecular characterization of rhizoctonia oryzae sativae in Bangladesh

Aggregate sheath spot disease of rice caused by Rhizoctonia oryzaesativae has emerged in higher incidence in North-Western region of Bangladesh. Thirty isolates of R. oryzae-sativae were studied by using morphological and molecular marker. Isolates were confirmed using specific primer where a single band of 1200bp was amplified. Two distinct groups relatively slow and faster were found in mycelal growth. Molecular characterization was done using four primers and DNA band ranged from 0.25 to 2.21 kb. A combined dendrogram was constructed which separated the isolates into three groups at 69.6% similarity level. All isolates placed in two major clusters except isolate RA-1 placed in cluster group III but were not grouped according to their geographic origins. Fast growing isolates have been placed in Group II while slow growing isolates in cluster group I. The similarity coefficient values of the dendrogram profile ranged from 0.36 to 0.98 with an average of 0.67. Diversity of different isolate showed that significant variation was present among the isolate and were not genetically identical.&#x0D; SAARC J. Agri., 16(2): 119-128 (2018)

Genetic diversity using RAPD markers, mineral composition and their correlation in selected local landraces of finger millet [Eleusine coracana (L.) Gaertn.

Twelve local cultivars of finger millet [Eleusine coracana (L.) Gaertn.] were evaluated for their molecular diversity and mineral composition. Twelve selected RAPD primers generated 162 amplified fragments with an average of 13.5 bands per primer and 86.08% polymorphism. The average PIC, EMR, MI and RP values were 0.30, 5.79, 1.75 and 5.97 respectively, per primer. The Jaccard’s similarity coefficient values ranged from 0.44 to 0.67. The dendrogram grouped 12 finger millet cultivars into three distinctive groups based on their divergence analysis which indicates that significant genetic diversity was present among the cultivars. Among the major elements, average calcium content was found to be highest in all the cultivars followed by sodium, sulphur and nitrogen. The average content of manganese was followed by zinc and iron between the minor elements. The correlation studies between genetic diversity using Shannon diversity index and mineral content showed positive correlation among all the minerals studied excluding nitrogen. Genetic diversity combined with mineral datasets will be useful for selection of suitable cultivars for improvement of nutritional values in finger millet varieties through conventional and molecular breeding approaches.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is challenging, and recent work has demonstrated that convolutional neural networks perform well for this task. In this study, we have investigated the performance of several two-dimensional (2D) U-Net and three-dimensional (3D) U-Net configurations using both fat-suppressed and nonfat-suppressed images. We have also assessed the effect of changing the number and quality of the ground truth segmentations. We designed eight studies to investigate the effect of input types and the dimensionality of the U-Net operations for the breast MRI segmentation. Our training data contained 70 whole breast volumes of T1-weighted sequences without fat suppression (WOFS) and with fat suppression (FS). For each subject, we registered the WOFS and FS volumes together before manually segmenting the breast to generate ground truth. We compared four different input types to the U-nets: WOFS, FS, MIXED (WOFS and FS images treated as separate samples), and MULTI (WOFS and FS images combined into a single multichannel image). We trained 2D U-Nets and 3D U-Nets with these data, which resulted in our eight studies (2D-WOFS, 3D-WOFS, 2D-FS, 3D-FS, 2D-MIXED, 3D-MIXED, 2D-MULTI, and 3D-MULT). For each of these studies, we performed a systematic grid search to tune the hyperparameters of the U-Nets. A separate validation set with 15 whole breast volumes was used for hyperparameter tuning. We performed Kruskal-Walis test on the results of our hyperparameter tuning and did not find a statistically significant difference in the ten top models of each study. For this reason, we chose the best model as the model with the highest mean dice similarity coefficient (DSC) value on the validation set. The reported test results are the results of the top model of each study on our test set which contained 19 whole breast volumes annotated by three readers fused with the STAPLE algorithm. We also investigated the effect of the quality of the training annotations and the number of training samples for this task. The study with the highest average DSC result was 3D-MULTI with 0.96±0.02. The second highest average is 2D WOFS (0.96±0.03), and the third is 2D MULTI (0.96±0.03). We performed the Kruskal-Wallis one-way ANOVA test with Dunn's multiple comparison tests using Bonferroni P-value correction on the results of the selected model of each study and found that 3D-MULTI, 2D-MULTI, 3D-WOFS, 2D-WOFS, 2D-FS, and 3D-FS were not statistically different in their distributions, which indicates that comparable results could be obtained in fat-suppressed and nonfat-suppressed volumes and that there is no significant difference between the 3D and 2D approach. Our results also suggested that the networks trained on single sequence images or multiple sequence images organized in multichannel images perform better than the models trained on a mixture of volumes from different sequences. Our investigation of the size of the training set revealed that training a U-Net in this domain only requires a modest amount of training data and results obtained with 49 and 70 training datasets were not significantly different. To summarize, we investigated the use of 2D U-Nets and 3D U-Nets for breast volume segmentation in T1 fat-suppressed and without fat-suppressed volumes. Although our highest score was obtained in the 3D MULTI study, when we took advantage of information in both fat-suppressed and nonfat-suppressed volumes and their 3D structure, all of the methods we explored gave accurate segmentations with an average DSC on >94% demonstrating that the U-Net is a robust segmentation method for breast MRI volumes.

Molecular characterization of Asplenium nidus L. – A potential ornamental fern species from Andaman

Asplenium nidus L. is one of the most important terrestrial fern in Andaman and Nicobar Islands with great ornamental value. Inter simple sequence repeat (ISSR) analysis was used to study the genetic diversity of the species A. nidus L. collected from different geographical locations of the Island. Among 15 ISSR primers screened, 9 primers produced amplification and produced a total of 222 reproducible amplified bands. The number of amplified bands varied from 10 in ISSR 11 to 44 in UBC 842 with an average of 24.7 bands per primer. The range of polymorphic bands and percent polymorphism observed were 1 to 5 and 33.3 to 83.3, respectively. The polymorphic information content value of ISSR marker ranged from 0.365 to 0.485. Cluster analysis grouped the twelve accessions into three clusters (Cluster I, II and III) at Jaccard’s similarity coefficient value of 0.69 on basis of geographical locations of the accessions. The cluster I was further divided into two sub-clusters Ia and Ib at the coefficient value of 0.77 and it includes the accessions belonging to South Andaman, one each accession collected from two different locations, Little Andaman and Rangat (Middle Andaman) were grouped separately as clusters II and III, respectively. The genetic variability studies with ISSR marker grouped the accessions of A. nidus L.on basis of their geographical locations and it would help in rapid identification of polymorphism and assist in germplasm collection, conservation and domestication programmes.

Harmonization of practices between radiotherapy centres in the Nord and Pas-de-Calais regions (France): A three-year evaluation

PurposeThe delineation of volumes of interest can be a source of significant interobserver variability. The purpose of this study was to improve the homogeneity of delineation between oncologist-radiotherapists in the territorial departments of Nord and Pas-de-Calais (France) through discussions of clinical cases and the adoption of common published reference documents. Materials and methodsAll eleven radiotherapy centres in the Nord and Pas-de-Calais departments of France participated. The localizations assessed to date included prostate, head and neck, breast and brain cancers. For each localization, the junior or senior physician(s) in charge of pathology delineated the volumes of interest according to their usual practices. Validated indices, including the Dice similarity coefficient, were used to quantify the delineation differences. The anonymized results were presented at two to three annual meetings. A second delineation of the clinical cases was then carried out to quantify homogenization. An evaluation of dosimetry practices was also conducted for prostate cancer. Wilcoxon assay matched data were used. ResultsOur work showed either satisfactory delineation concordance after the initial assessment or improved delineation concordance. For prostate cancer, the Dice similarity coefficient values were greater than 0.6 initially in two of the three clinical cases. For head and neck cancers, a statistically significant improvement was observed for only one of the clinical target volumes. More than half of the Dice similarity coefficient values were greater than 0.6 in the first comparison. The study of clinical cases of breast cancer allowed a homogenization of the delineation of five of the six lymph node clinical target volumes. The dosimetry study of prostate cancer allowed for a homogenization of practices. ConclusionThis work makes it possible to harmonize the delineation practices around validated standards. An extension to the entire Hauts-de-France region is planned.

A Distance Regularized Level-Set Evolution Model Based MRI Dataset Segmentation of Brain’s Caudate Nucleus

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation.

Genetic diversity of pink bollworm Pectinophora Gossypiella (Saunders) populations

Present study was conducted to determine the genetic diversity of Pectinophora gossypiella (Saunders) larvae collected from different cotton growing regions of India by RAPD-PCR analysis. RAPD decamer primers viz., OPI-11 to OPI-20 were used out of which four (OPI-11-OPI-14) produced amplicon of good resolution and sufficient variation. A total of 153 amplicons produced out of which 118 (92%) are polymorphic bands indicating high genetic polymorphism among the P. gossypiella populations. The similarity coefficient values ranged from 0.20 to 0.76 for the pair-wise combination among twelve populations of P. gossypiella. An UPGMA dendrogram generated based on Jaccard's similarity coefficient for 12 populations grouped into two clusters. Minimum similarity (0.20) was observed between LAB and GJJ populations. The present investigation shows occurrence of high genetic polymorphism and low genetic diversity among the P. gossypiella populations in India.

Keanekaragaman dan Hubungan Kekerabatan Pisang (Musaceae) di Kota Bengkulu, Provinsi Bengkulu

This study was to determine the diversity and banana phylogeny (Musaceae) in Bengkulu City, Bengkulu Province. The results of this study are expected to be an information material for development to achieve improvements in banana varieties / cultivars. Collection of exploration study data in selected Bengkulu City, namely 3 Districts of Muara Bangkahulu, Sungai Serut, and Teluk Segara. The location of specimen collection consists of wild and cultivated plants in the yard and a community garden in the city of Bengkulu. Morphological data were compiled based on observations of morphological characters from specimens in the field, characters adapted from banana descriptors. Morphological data were compiled based on observations of specimens, the results of morphological characters were analyzed to form fenogram to see phylogeny, using the NTSYS-pc 2.02 program. 22 collections and 10 varieties of bananas (Musaceae), wild bananas and cultivation by the community were found in 3 sub-districts of Muara Bangkahulu, Sungai Serut, and Teluk Segara. Cultivated bananas that are planted in general, the community planted in the home yard for home-based commodities. The relationship between wild bananas (Musaceae) and cultivation in the city of Bengkulu, divided into two main groups, namely group A, namely the type of Musa acuminata Colla and group B is the type of Musa acuminata x Musa balbisiana. Both of these groups have similarity coefficient values of phylogeny between 0.45 to 0.60.

Genetic diversity of Bletilla striata assessed by SCoT and IRAP markers

BackgroundBletilla striata is a well-known traditional Chinese herb with varieties of functions. In China, the natural resources of Bletilla striata have been severely damaged because of the excessive exploitation and destruction of natural habitats. The aim of present study was to provide a reference for fully exploiting and utilizing the germplasm resources of Bletilla striata.ResultsThe genetic diversity of 50 varieties of Bletilla striata from different area in China was analyzed by SCoT and IRAP molecular marker technique. A total of 209 bands were amplified by 20 groups of SCoT primers, of which 201 (96.17%) were polymorphic, and 47 polymorphic bands (94%) were observed in 50 bands amplified by 8 groups of IRAP primers. The 50 populations of Bletilla striata were divided into two major groups by SCoT and IRAP at the genetic similarity coefficient value of 0.60 and 0.68 individually. The partition of clusters in the unweighted pair group method with arithmetic mean dendrogram and principal coordinate analysis plot based on the SCoT and IRAP markers was similar.ConclusionsResults indicated the abundant genetic diversity of Bletilla striata among different areas. Our results will provide useful information for resource protection.

External Validation of a Deep Learning-Based Auto-Segmentation Method for Radiation Therapy

Automated delineation of the organs-at-risk (OARs) has high application value for treatment planning in radiotherapy due to the efficiency and consistency. We previously reported a Deep Dilated Convolutional Neural Networks (DDCNN)-based auto-segmentation method for radiotherapy of rectal cancer. The DDCNN model was constructed and implemented with data from a single institution. In this study, we investigated the reproducibility of DDCNN using independent validation samples from a public dataset for external validation. The DDCNN was trained using data of 218 patients from institution A. We performed model testing with 60 patients from institution A (internal validation dataset), and 18 patients with expert consensus delineations from a public dataset (external validation dataset). All patients from institution A were set up with the prone position. While the patients from the external validation dataset were set up in the supine position. The images were processed geometrically to be consistent with the positioning required by the model (prone vs. supine). A histogram normalization operation was applied to ensure the consistency of the imaging parameters. The performance of DDCNN was evaluated on segmentation of the bladder, left femoral head, and right femoral head. The segmentation accuracy was consistent for both internal and external cases. The Dice Similarity Coefficient (DSC) values were 0.93±0.03 vs. 0.89±0.07 for the bladder, 0.92±0.04 vs. 0.90±0.05 for the left femur, and 0.92±0.03 vs. 0.91±0.05 for the right femur, respectively. For the bladder, the accuracy of segmentation from external validation was significantly worse than that from internal validation (p<0.05). However, the lower dice value was due to the different filling levels of the bladder and the contrast agents remaining in the bladder in some cases of external validation set. We selected the cases that had high consistency with the training set through visual inspection and evaluated the segmentation accuracy. The DSC value was 0.92±0.03, which was comparable with that of the internal validation. Despite different patient positioning, patient sizes, and imaging parameters, DDCNN showed overall comparable agreements on segmentation of the OARs with clear boundaries between the internal and external validation sets. The external validation demonstrates that the deep learning method has the potential for general application in radiotherapy, even though its performance in segmentation of organs with considerable inter- and intra-observer variability needs further investigation.

Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network.

Objectives To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). Materials and Methods In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. Results The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. Conclusions We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.

STUDI KOMPARASI KARAKTER MORFOLOGI DAN ISOZIM DUKU Lansium parasiticum (Osbeck) K.C. Sahni & Bennet ASAL KABUPATEN KUANTAN SINGINGI, RIAU

Duku ( Lansium parasiticum (Osbeck) K.C. Sahni & Bennet) from Kuantan Singingi Regency, Riau, known by local people as duku turak, has a unique oval betel nut-like neck, and tastes sweet similar to round duku. It has thick skin that is not easy to break, nor quickly rot. This duku was more preferred by local people than the round shaped duku known as duku gondok. However, the information about the potential of duku turak has not been known widely. The aim of this study was to compare morphology and isozyme characteristics of duku turak and gondok growing in Kuantan Singingi Regency based on morphological and isozyme characteristics. The result showed that turak and gondok can be distinguished based on morphological characteristics i.e tree, leave and fruit shape. Analysis of isozyme banding patterns used esterase isozyme which shows variation in the resulting patterns formed two bands. Acid phospatase (ACP) isozyme formed eight bands and aspartate aminotransferase (AAT) isozyme formed only one band. However, peroksidase isozyme showed no different variation in the resulting patterns. Similarity coefficient value based on morphology characters was about 0.15 – 0.74, while value of isozyme analysis was about 0.29 – 1.00 as well as the combination among morphology characters and isozyme were about 0.24 – 0.71. The results of this study provide characteristic information and taxonomic groupings that can be useful as a type improvement program that relies heavily on available genetic diversity

Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy

Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of different patients are of different sizes. Thus, small tumors may be ignored while big tumors may exceed the receptive fields of convolutions. The purpose of this study is to further improve the segmentation accuracy using a novel CNN (named CAC–SPP) with cascaded atrous convolution (CAC) and a spatial pyramid pooling (SPP) module. This work is the first attempt at applying SPP for segmentation in radiotherapy. We improved the network based on ResNet-101 yielding accuracy gains from a greatly increased depth. We added CAC to extract a high-resolution feature map while maintaining large receptive fields. We also adopted a parallel SPP module with different atrous rates to capture the multi-scale features. The performance was compared with the widely adopted U-Net and ResNet-101 with independent segmentation of rectal tumors for two image sets, separately: (1) 70 T2-weighted MR images and (2) 100 planning CT images. The results show that the proposed CAC–SPP outperformed the U-Net and ResNet-101 for both image sets. The Dice similarity coefficient values of CAC–SPP were 0.78 ± 0.08 and 0.85 ± 0.03, respectively, which were higher than those of U-Net (0.70 ± 0.11 and 0.82 ± 0.04) and ResNet-101 (0.76 ± 0.10 and 0.84 ± 0.03). The segmentation speed of CAC–SPP was comparable with ResNet-101, but about 36% faster than U-Net. In conclusion, the proposed CAC–SPP, which could extract high-resolution features with large receptive fields and capture multi-scale context yields, improves the accuracy of segmentation performance for rectal tumors.

Measurement of the radial diffusion coefficient of galactic cosmic rays near the Earth by the GRAPES-3 experiment

The flux of galactic cosmic rays (GCRs) is isotropic in the interstellar space. However, in the heliosphere, the ram pressure of outward-moving solar wind convects the GCRs away from the Sun, thereby producing a density gradient in the radial direction. The diffusion of GCRs due to this gradient and scattering with the irregularities in the interplanetary magnetic field (IMF) induce variations in their flux that can be observed near the Earth. A framework for the diffusion-convection mechanism of GCR propagation developed by Parker and collaborators [Phys. Rev. 110, 1445 (1958); Planet. Space Sci. 13, 9 (1965); Astrophys. J. 772, 46 (2013); Space Sci. Rev. 78, 401 (1996); Astrophys. J. 234, 746 (1979); Astrophys. J. 361, 162 (1990); Space Sci. Rev. 176, 299 (2013)] offers a good description of this phenomenon. One of the outcomes of this framework is an anticorrelation of the variation in solar wind velocity (${\mathrm{V}}_{\mathrm{SW}}$) and the GCR flux. A second outcome of this gradient in the presence of IMF is the movement of GCRs perpendicular to the ecliptic plane called ``Swinson flow.'' Therefore, (i) the correlated variations of ${\mathrm{V}}_{\mathrm{SW}}$ and GCR flux and (ii) the GCR radial density gradient obtained from Swinson flow can each be used to independently measure the radial diffusion coefficient of GCRs in the inner heliosphere. In an earlier work [Phys. Rev. D 91, 121303(R) (2015)], the GCR flux was shown to be anticorrelated with ${\mathrm{V}}_{\mathrm{SW}}$ at $(\ensuremath{-}1.33\ifmmode\pm\else\textpm\fi{}0.07)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}%(\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}{)}^{\ensuremath{-}1}$. This anticorrelation yields a radial diffusion coefficient $\ensuremath{\kappa}=0.97\ifmmode\times\else\texttimes\fi{}{10}^{19}\text{ }\text{ }{\mathrm{m}}^{2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ at 1 AU. In another work [Astropart. Phys. 62, 21 (2015)], the measurement of Swinson flow was used to obtain a GCR radial density gradient of $0.65\text{ }\text{ }{\mathrm{A}\mathrm{U}}^{\ensuremath{-}1}$ at a median rigidity of 77 GV. Here, we report a measurement of radial diffusion coefficient $\ensuremath{\kappa}=1.04\ifmmode\times\else\texttimes\fi{}{10}^{19}\text{ }\text{ }{\mathrm{m}}^{2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ at 1 AU from the above-mentioned density gradient, for a mean ${\mathrm{V}}_{\mathrm{SW}}$ of $450\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$. Thus, these two distinct approaches essentially yielded similar values of the radial diffusion coefficient $\ensuremath{\kappa}={10}^{19}\text{ }\text{ }{\mathrm{m}}^{2}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ at 1 AU, characterizing the diffusion of GCRs at 77 GV. From this value of $\ensuremath{\kappa}$, the mean free path length for parallel diffusion ${\ensuremath{\lambda}}_{\ensuremath{\parallel}}$ was estimated to be 1.2 AU at 77 GV, consistent with earlier reports [Rev. Geophys. Space Phys. 20, 335 (1982); Astrophys. J. 420, 294 (1994); Astrophys. J. 604, 861 (2004)].

Correlating karyomorphology and molecular marker analyses in turmeric: a case study

ABSTRACT India is the largest producer and exporter of turmeric (Curcuma longa), and different cultivars of this crop are traditionally characterized using agronomic traits, such as rhizome weight, yield potential, color and aroma of rhizome, curcumin content, and stability of curcumin content, which are influenced by environmental factors. The purpose of the present study was to probe whether karyomorphological and molecular marker attributes of turmeric cultivars could be correlated to validate their distinctiveness. Detailed karyomorphological analyses revealed uniform somatic chromosome number (2n = 63), with symmetric karyotype in all the studied cultivars. Chromosomes varied from 0.67 to 2.03 µm in length, with up to 72% bearing primary constrictions in the nearly median region (i-value ranging from 27.17 to 50). Asymmetry indices also indicated overall karyotypic symmetry among cultivars. On the contrary, random amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) marker analyses revealed significant variability in the genetic constitution among them; Jaccard’s similarity coefficient value ranged from 0.569 to 0.969. Unweighted pair group method with arithmetic average (UPGMA) dendrogram and principal coordinate analysis based on the Jaccard’s coefficient produced four distinct clusters, which showed no geographical bias. Thus, it was concluded that although significant genetic variability that exists among the turmeric cultivars can be utilized for examining their authenticity, practicality of the use of karyomorphological characters for identification is significantly limited by the gross karyotypic similarity. Cryptic genetic changes accumulate in the genome of these plants because of their interaction with the environment. However, these remain confined within each cultivar in the absence of large-scale hybridizations and predominant vegetative propagation. This probably leads to variability in agronomic characters and may even help in developing novel characters in different cultivars. However, detection of any such change in the chromosomal structure would require fluorescent hybridization techniques.

Role of Daily Plan Adaptation in MR-Guided Stereotactic Ablative Radiation Therapy for Adrenal Metastases

To study interfractional organ changes during magnetic resonance (MR)-guided stereotactic ablative radiation therapy for adrenal metastases and to evaluate the dosimetric advantages of online plan adaptation. Seventeen patients underwent a total of 84 fractions of video-assisted, respiration-gated, MR-guided adaptive radiation therapy to deliver either 50 Gy (5 fractions), 60 Gy (8 fractions), or 24 Gy (3 fractions). An MR scan was repeated before each fraction, followed by rigid coregistration to the gross tumor volume (GTV) on the pretreatment MR scan. Contour deformation, planning target volume (PTV) (GTV + 3 mm) expansion, and online plan reoptimization were then performed. Reoptimized plans were compared with baseline treatment plans recalculated on the anatomy-of-the-day ("predicted plans"). Interfractional changes in organs at risk (OARs) were quantified according to OAR volume changes within a 3 cm distance from the PTV surface, center of mass displacements, and the Dice similarity coefficient. Plan quality evaluation was based on target coverage (GTV and PTV) and high dose sparing of all OARs (V36Gy, V33Gy, and V25Gy). Substantial center of mass displacements were observed for stomach, bowel, and duodenum, 17, 27 and 36 mm, respectively. Maximum volume changes for the stomach, bowel, and duodenum within 3 cm of PTV were 23.8, 20.5, and 20.9 cm3, respectively. Dice similarity coefficient values for OARs ranged from 0.0 to 0.9 for all fractions. Baseline plans recalculated on anatomy-of-the-day revealed underdosage of target volumes and variable OAR sparing, leading to a failure to meet institutional constraints in a third of fractions. Online reoptimization improved target coverage in 63% of fractions and reduced the number of fractions not meeting the V95% objective for GTV and PTV. Reoptimized plans exhibited significantly better sparing of OARs. Significant interfractional changes in OAR positions were observed despite breath-hold stereotactic ablative radiation therapy delivery under MR-guidance. Online reoptimization of treatment plans led to significant improvements in target coverage and OAR sparing.