Dice Similarity Index Research Articles

Human Leucocyte Antigen Profile and Transmission of Mutans Streptococci in Mother-Child Pairs.

To investigate possible association between the transmission of mutans streptococci and sharing the immune system component Human Leucocyte Antigen (HLA) class II in mother-child pairs. Plaque samples from 43 mother-child pairs were cultivated and screened for mutans streptococci. In 14 pairs where both mother and child harboured the bacteria, the strains were genotyped by Random Amplified Polymorphic DNA and samples were run on PAGE gels. Analysis of genetic identity between mother and child strains was performed with help of software and Dice similarity index. The distribution of HLA of serogroup DR4 (HLA DR4) was studied in relation to maternal transmission and mutans streptococci colonisation in children. The study hypothesis was that in pairs where both mother and child were HLA DR4 positive, transmission of mutans streptococci was more likely. No correlation between the presence of HLA DR4 in mother and child and maternal transmission of mutans streptococci was established. However, the results showed no linkage between mutans streptococci colonisation and HLA DR 4. Of 15 children with mutans streptococci, 12 were HLA DR4 positive. The result suggests that presence of HLA DR4 could be a predisposing factor for colonisation with mutans streptococci in children.

Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses

The irritative zone - the area generating epileptic spikes - can be studied non-invasively during the interictal period using Electrical Source Imaging (ESI) and simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI). Although the techniques yield results which may overlap spatially, differences in spatial localization of the irritative zone within the same patient are consistently observed. To investigate this discrepancy, we used Blood Oxygenation Level Dependent (BOLD) functional connectivity measures to examine the underlying relationship between ESI and EEG-fMRI findings. Fifteen patients (age 20-54), who underwent presurgical epilepsy investigation, were scanned using a single-session resting-state EEG-fMRI protocol. Structural MRI was used to obtain the electrode localisation of a high-density 64-channel EEG cap. Electrical generators of interictal epileptiform discharges were obtained using a distributed local autoregressive average (LAURA) algorithm as implemented in Cartool EEG software. BOLD activations were obtained using both spike-related and voltage-map EEG-fMRI analysis. The global maxima of each method were used to investigate the temporal relationship of BOLD time courses and to assess the spatial similarity using the Dice similarity index between functional connectivity maps. ESI, voltage-map and spike-related EEG-fMRI methods identified peaks in 15 (100%), 13 (67%) and 8 (53%) of the 15 patients, respectively. For all methods, maxima were localised within the same lobe, but differed in sub-lobar localisation, with a median distance of 22.8mm between the highest peak for each method. The functional connectivity analysis showed that the temporal correlation between maxima only explained 38% of the variance between the time course of the BOLD response at the maxima. The mean Dice similarity index between seed-voxel functional connectivity maps showed poor spatial agreement. Non-invasive methods for the localisation of the irritative zone have distinct spatial and temporal sensitivity to different aspects of the local cortical network involved in the generation of interictal epileptiform discharges.

Image analysis for brain tumour detection using GA-SVM with auto-report generation technique

In this study, we have presented image analysis for the brain tumour segmentation and detection based on Berkeley wavelet transformation, enabled by genetic algorithm and support vector machine. The proposed system uses double classification analysis to conclude tumour type. The proposed system also investigated auto-report generation technique using user-friendly graphical user interface in MATLAB. The experimental results of proposed technique is been evaluated and validated for performance and quality analysis on magnetic resonance (MR) medical images based on accuracy, sensitivity, specificity and dice similarity index coefficient. The experimental results achieved 97.77% accuracy, 98.98% sensitivity, 94.44% specificity and an average of 0.9849 dice similarity index coefficient, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from MR images. The experimental result is validated by extracting 89 features and selecting the relevant features appropriately using genetic algorithm optimise by support vector machine.

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing the number of voxels to analyze and removing foreign tissues and air. In recent years, the deep convolutional neural networks (CNNs) enabled a sensible improvement in many visual tasks automation, such as image classification and object recognition. These advances also involved radiomics, enabling high-throughput extraction of quantitative features, resulting in a strong improvement in automatic diagnosis through medical imaging. However, machine learning and, in particular, deep learning approaches are gaining popularity in the radiomics field for tissue segmentation. This work aims to accurately segment breast parenchyma from the air and other tissues (such as chest-wall) by applying an ensemble of deep CNNs on 3D MR data. The novelty, besides applying cutting-edge techniques in the radiomics field, is a multi-planar combination of U-Net CNNs by a suitable projection-fusing approach, enabling multi-protocol applications. The proposed approach has been validated over two different datasets for a total of 109 DCE-MRI studies with histopathologically proven lesions and two different acquisition protocols. The median dice similarity index for both the datasets is 96.60 % (±0.30 %) and 95.78 % (±0.51 %) respectively with p < 0.05, and 100% of neoplastic lesion coverage.

Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

The main aim of digital image segmentation for portioned the image in to its constituents parts for getting information regarding features of image also used to get pathological details from medical images. The literature available from last two decades the important scheme for image segmentation is with Level Set technique, multilevel thresholding of gray scale on histogram of image is also a traditional method of image segmentation. In this paper low contrast images from medical and satellite images considered for image segmentation to extract features. This paper puts forward a novel image segmentation method via Level Set Function along with Bi-Histogram Equalization based on Harmony Search Algorithm(LSFBHEHS). The Selective Binary and Gaussian Filtering Regularised Level Set (SBGFRLS) is efficient novel region based Active Contour Model, it uses a novel region-based signed pressure force (SPF) function, it can adeptly halt the contours at blurred edges and weak edges. Other important advantage is internal and external boundaries can be distinguished by fixing the initial contour may be anyplace in the considered image. This method is resourceful but requires more time and inefficient for segmentation of low contrast images. This problem is rectified by applying bi-histogram equalization(BHE) image enhancement method prior to Level Set, it can be treated as pre-processing. In BHE technique of image enhancement, the image histogram is partitioned into two divisions based optimized gray level threshold , and equalize each part of histogram separately and combined later. To find the optimized threshold level to slice the histogram into two parts, Otsu’s multilevel thresholding method used to find threshold level, to find optimized thresholding level Harmony Search Algorithm(HSA) is implemented to maximize inert class variance as objective function. For evaluating the proposed method and SBGFRLS, the qualitative measured used like Dice similarity index, Measure of Enhancement(EME) and time required, for experimentation numerous low contrast satellite and medical images are considered, results clarified that the proposed method is more efficient for low contrast and inhomogeneous images.

Evaluating tectonic models for the formation of the North American Cordillera using multivariate statistical analysis of Late Triassic conodont faunas

The tectonic processes that led to the current configuration of the Quesnel and Stikine terranes within the North American Cordillera are contentious, with competing models favouring either northward, strike-slip movement or southward, rotational movement of the Stikine terrane. Multivariate statistical analysis of Late Triassic conodonts faunas from Canada and the USA has been used to evaluate these competing models and to constrain the movement of terranes relative to the North American continent. Late Carnian to early Norian conodonts are present in both the Stikine and Quesnel terranes, as well as at high and low latitudes on the North American continental margin, in northeastern British Columbia and Nevada respectively. The conodont faunas from each of these four geographic areas have been compared using the Dice, Jaccard, Ochiai and Simpson similarity indices. The highest values of similarity were found to be between the faunas of the Stikine and Quesnel terranes, supporting previous conclusions that these belonged to a single volcanic arc system. The conodont faunas of both of these terranes were more similar to that of northeastern British Columbia than either were to that of Nevada, suggesting that during the Late Triassic the terranes were more likely located at higher latitudes than lower latitudes. Finally, the conodont fauna of the Stikine terrane was significantly more similar to that of Nevada than the fauna of the Quesnel terrane was, which indicates that of the two terranes, the Stikine terrane lay farther south than the Quesnel terrane during the Late Triassic. This interpretation of the orientation of these terranes supports tectonic models that favour northward strike-slip translation of the Stikine terrane, rather than those models that require southward rotation of the terrane.

Diffusion Tensor Imaging of the Kidney: Design and Evaluation of a Reliable Processing Pipeline

Diffusion tensor imaging (DTI) is particularly suitable for kidney studies due to tubules, collector ducts and blood vessels in the medulla that produce spatially restricted diffusion of water molecules, thus reflecting the high grade of anisotropy detectable by DTI. Kidney DTI is still a challenging technique where the off-resonance susceptibility artefacts and subject motion can severely affect the reproducibility of results. The aim of this study is to design a reliable processing pipeline by assessing different image processing approaches in terms of reproducibility and image artefacts correction. The results of four different processing pipelines (eddy: correction of eddy-currents and motion between DTI volume; eddy-s2v: eddy and within DTI volume motion correction; topup: eddy and geometric distortion correction; topup-s2v: topup and within DTI volume motion correction) are compared in terms of reproducibility by test-retest analysis in 14 healthy subjects. Within-subject coefficient of variation (wsCV) and intra-class correlation coefficient (ICC) are measured to assess the reproducibility and Dice similarity index is evaluated for the spatial alignment between DTI and anatomical images. Topup-s2v pipeline provides highest reproducibility (wsCV = 0.053, ICC = 0.814) and best correction of image distortion (Dice = 0.83). This study definitely provides a recipe for data processing, enabling for a clinical suitability of kidney DTI.

Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier

Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke patients. Precise detection of brain lesions is an important task related to impaired behavior. In this paper, we aimed to develop an automatic method to segment and classify infarct lesion in diffusion-weighted imaging (DWI) of brain MRI. The method includes hybrid fuzzy [Formula: see text]-means (HFCM) clustering in which the structure of [Formula: see text]-means clustering is modified with rough sets and fuzzy sets to improve the segmentation performance with self-adjusted intensity thresholds. Quantitative evaluation was carried out on 128 MRI slices of brain image collected from ischemic stroke patients at the Department of Radiology, IMS and SUM Hospital, Bhubaneswar. The informative statistical features have been extracted using gray-level co-occurrence matrix (GLCM) and used to classify the types of stroke infarct according to the Oxfordshire Community Stroke Project (OCSP) classification. The parameters such as accuracy, Dice similarity index (DSI) and Jaccard index (JI) were utilized to evaluate the effectiveness of the proposed method in detecting the stroke lesions. The segmentation method achieved the average accuracy, DSI and JI of 96.8%, 95.8% and 92.2%, respectively, in support vector machine (SVM) classifier. The obtained results are higher in terms of random forest (RF) classification. With a high Dice coefficient of 0.958 and other evaluated parameters, the proposed method outperforms earlier published results.

Multi-target Interactive Neural Network for Automated Segmentation of the Hippocampus in Magnetic Resonance Imaging

The hippocampus has been recognized as an important biomarker for the diagnosis and assessment of neurological diseases. Convenient and accurate automated segmentation of the hippocampus facilitates the analysis of large-scale neuroimaging studies. This work describes a novel technique for hippocampus segmentation in magnetic resonance images, in which interactive neural network (Inter-Net) is based on 3D convolutional operations. Inter-Net achieves the interaction through two aspects: one is the compartments, which builds an exponential ensemble network that integrates numerous short networks together when forward propagation. The other is the pathways, which realizes inter-connection between feature extraction and restoration. In addition, a multi-target architecture is proposed by designing multiple objective functions in terms of evaluation index, information theory, and data distribution. The proposed architecture is validated in fivefold cross-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, where the mean Dice similarity indices of 0.919 (± 0.023) and precision of 0.926 (± 0.032) for the hippocampus segmentation. The running time is approximately 42.1 s from reading the image to outputting the segmentation result in our computer configuration. We compare the experimental results of a variety of methods to prove the effectiveness of the Inter-Net and contrast integrated architectures with different objective functions to illustrate the robustness of the fusion. The proposed framework is general and can be easily extended to numerous tissue segmentation tasks while it is tailored for the hippocampus.

Auto-segmentation of head and neck organs at risk in radiotherapy and its dependence on anatomic similarity.

PurposeThe aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours.MethodsA sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM).ResultsThe mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures.ConclusionImproved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

Brachiopod palaeobiogeography in the western Tethys during the Early Jurassic diversity maximum: introduction of a Pontic Province

The biogeography of Pliensbachian (Early Jurassic) brachiopods in western Tethys is investigated using complementary multivariate tools including metric and non-metric ordination, additive cluster and bootstrapped spanning network analyses, as well as one-way analysis of similarity and similarity percentage analysis. All analyses were conducted using the Dice and Simpson similarity indices for presence/absence data on occurrence datasets involving 24 assemblages homogenized at the species level, including (403 taxa) or excluding (210 taxa) species found in only one assemblage. The analyses present a highly consistent biogeographical picture involving three main clusters: the Euro-Boreal, Mediterranean and Pontic biochores. The brachiopod species typical of the newly defined Pontic biochore are illustrated. The three assemblages from the Atlas area are interpreted as a fourth biochore, compositionally intermediate between those of the Euro-Boreal and Mediterranean. The Mediterranean biochore can be further divided into an intra- and a peri-Mediterranean group. These five, palaeogeographically well-constrained biochores show moderate to high degrees of species endemicity, ranging from 20% (Atlas) to 58% (Euro-Boreal). Based on available evidence, and after a reasonable cutback of the customary scale of ranks, the following biogeographical categories and names are suggested for the western Tethyan Pliensbachian brachiopod biochores: Euro-Boreal Province, Mediterranean Province (including an intra-Mediterranean and a peri-Mediterranean Subprovince), Pontic Province and Atlas Subprovince. In addition, a still poorly documented brachiopod biochore occurs on the Gondwana margin as a possible precursor of the extensive Middle to Late Jurassic Ethiopian Province.

Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks.

Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50–80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.

Detection of microaneurysms using ant colony algorithm in the early diagnosis of diabetic retinopathy

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way.

Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening

We present a novel and fully automated fundus image processing technique for glaucoma prescreening based on the rim-to-disc ratio (RDR). The technique accurately segments the optic disc and optic cup and then computes the RDR based on which it is possible to differentiate a normal fundus from a glaucomatous one. The technique performs a further categorization into normal, moderate, or severely glaucomatous classes following the disc-damage-likelihood scale (DDLS). To the best of our knowledge, this is the first engineering attempt at using RDR and DDLS to perform glaucoma severity assessment. The segmentation of the optic disc and cup is based on the active disc, whose parameters are optimized to maximize the local contrast. The optimization is performed efficiently by means of a multiscale representation, accelerated gradient-descent, and Green’s theorem. Validations are performed on several publicly available databases as well as data provided by manufacturers of some commercially available fundus imaging devices. The segmentation and classification performance is assessed against expert clinician annotations in terms of sensitivity, specificity, accuracy, Jaccard, and Dice similarity indices. The results show that RDR based automated glaucoma assessment is about 8% to 10% more accurate than a cup-to-disc ratio (CDR) based system. An ablation study carried out considering the ground-truth expert outlines alone for classification showed that RDR is superior to CDR by 5.28% in a two-stage classification and about 3.21% in a three-stage severity grading.

Deep learning for variational multimodality tumor segmentation in PET/CT

Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: (1) Only a few training samples were needed for training the designed network to produce the probability map; (2) The proposed method can be applied to small datasets, normally seen in clinic research; (3) The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); (4) The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ± 0.05, sensitivity (SE) of 0.86 ± 0.07, positive predictive value (PPV) of 0.87 ± 0.10, volume error (VE) of 0.16 ± 0.12, and classification error (CE) of 0.30 ± 0.12.

Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.

PET and CT are widely used imaging modalities in radiation oncology. PET imaging has a high contrast but blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between tumor and soft normal tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve segmentation accuracy. These information can be either consistent or inconsistent in the image-level. How to correctly localize tumor edges with these inconsistent information is a major challenge for co-segmentation methods. In this study, we proposed a novel variational method for tumor co-segmentation in PET/CT, with a fusion strategy specifically designed to handle the information inconsistency between PET and CT in an adaptive way - the method can automatically decide which modality should be more trustful when PET and CT disagree to each other for localizing the tumor boundary. The proposed method was constructed based on the Γ-convergence approximation of the Mumford-Shah (MS) segmentation model. A PET restoration process was integrated into the co-segmentation process, which further eliminate the uncertainty for tumor segmentation introduced by the blurring of tumor edges in PET. The performance of the proposed method was validated on a test dataset with fifty non-small cell lung cancer patients. Experimental results demonstrated that the proposed method had a high accuracy for PET/CT co-segmentation and PET restoration, and can accurately estimate the blur kernel of the PET scanner as well. For those complex images in which the tumors exhibit Fluorodeoxyglucose (FDG) uptake inhomogeneity or even invade adjacent soft normal tissues, the proposed method can still accurately segment the tumors. It achieved an average dice similarity indexes (DSI) of 0.85 ± 0.06, volume error (VE) of 0.09 ± 0.08, and classification error (CE) of 0.31 ± 0.13.

Liver segmentation from low-radiation-dose pediatric computed tomography using patient-specific, statistical modeling.

The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM). The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations. The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2weeks and 7years. In the proposed method, the calculation time of the PA was about 9s for the single Gaussian method, while it was 337s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost. We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.

Inter-observer variability of clinical target volume delineation in definitive radiotherapy of neck lymph node metastases from unknown primary. A cooperative study of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) Head and Neck Group.

This study, promoted by Italian Association of Radiotherapy and Clinical Oncology (AIRO) Head and Neck Group, aimed to assess the current national practice of target volume delineation on a case of neck lymph node metastases from unknown primary evaluating inter-observer variability, in a setting of primary radiotherapy. A case of metastatic neck lymph node from occult primary was proposed to 17 radiation oncologists. A national reference RT center was identified and considered as benchmark. Participants were requested to delineate target volumes. A structured questionnaire was administered. A comparison between following parameters of the CTVs was performed: centroids distances, Dice similarity index (DSI), Jaccard index and mean distance to agreement (MDA). Volume expressed in cubic centimeters and CTVs cranio-caudal extension were evaluated. Sixteen of 17 radiation oncologists recommended three CTVs dose levels. (CTV HD, CTV ID and CTV LD); CTV ID was not delineated by one of the participants and by the reference center. The distance between the reference centroid and the mean centroid of CTVs HD was 1.09cm (0.36-3.99cm); for CTV LD, a mean centroids distance of 2.45 (0.27-4.83cm) was found, and for CTV HD, mean DSI is 0.48 and mean Jaccard index is 0.32 and MDA was 8.89mm. CTV LD showed a mean DSI of 0.46, mean Jaccard index of 0.31 and MDA of 14.87 when compared to the reference. Many aspects concerning treatment optimization of cervical nodes metastases from occult primary remain unclear, and we found a notable heterogeneity of global radiotherapy management reporting discordances both in target volume delineation and volume prescription.

Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm

To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using an unsupervised machine learning algorithm and to evaluate the association between intervening nidal brain parenchyma and radiation-induced changes (RICs) after stereotactic radiosurgery. Fully automated segmentation via unsupervised classification with fuzzy c-means clustering was used to analyze the AVM nidus on T2-weighted magnetic resonance imaging studies. The proportions of vasculature, brain parenchyma, and cerebrospinal fluid were quantified. These were compared with the results from manual segmentation. The association between the brain parenchyma component and RIC development was assessed. The proposed algorithm was applied to 39 unruptured AVMs in 39 patients (17 female and 22 male patients), with a median age of 27 years. The median proportion of the constituents was as follows: vasculature, 31.3%; brain parenchyma, 48.4%; and cerebrospinal fluid, 16.8%. RICs were identified in 17 of the 39 patients (43.6%). Compared with manual segmentation, the automated algorithm was able to achieve a Dice similarity index of 79.5% (sensitivity, 73.5%; specificity, 85.5%). RICs were associated with a greater proportion of intervening nidal brain parenchyma (52.0% vs. 45.3%; P= 0.015). Obliteration was not associated with greater proportions of nidal vasculature (36.0% vs. 31.2%; P= 0.152). The automated segmentation algorithm was able to achieve classification of the AVM nidus components with relative accuracy. Greater proportions of intervening nidal brain parenchyma were associated with RICs.

Validation of mean upper cervical cord area (MUCCA) measurement techniques in multiple sclerosis (MS): High reproducibility and robustness to lesions, but large software and scanner effects

IntroductionAtrophy of the spinal cord is known to occur in multiple sclerosis (MS). The mean upper cervical cord area (MUCCA) can be used to measure this atrophy. Currently, several (semi-)automated methods for MUCCA measurement exist, but validation in clinical magnetic resonance (MR) images is lacking.MethodsFive methods to measure MUCCA (SCT-PropSeg, SCT-DeepSeg, NeuroQLab, Xinapse JIM and ITK-SNAP) were investigated in a predefined upper cervical cord region. First, within-scanner reproducibility and between-scanner robustness were assessed using intra-class correlation coefficient (ICC) and Dice's similarity index (SI) in scan-rescan 3DT1-weighted images (brain, including cervical spine using a head coil) performed on three 3 T MR machines (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) in 21 subjects with MS and 6 healthy controls (dataset A). Second, sensitivity of MUCCA measurement to lesions in the upper cervical cord was assessed with cervical 3D T1-weighted images (3 T GE HDxT using a head-neck-spine coil) in 7 subjects with MS without and 14 subjects with MS with cervical lesions (dataset B), using ICC and SI with manual reference segmentations.ResultsIn dataset A, MUCCA differed between MR machines (p < 0.001) and methods (p < 0.001) used, but not between scan sessions. With respect to MUCCA values, Xinapse JIM showed the highest within-scanner reproducibility (ICC absolute agreement = 0.995) while Xinapse JIM and SCT-PropSeg showed the highest between-scanner robustness (ICC consistency = 0.981 and 0.976, respectively). Reproducibility of segmentations between scan sessions was highest in Xinapse JIM and SCT-PropSeg segmentations (median SI ≥ 0.921), with a significant main effect of method (p < 0.001), but not of MR machine or subject group. In dataset B, SI with manual outlines did not differ between patients with or without cervical lesions for any of the segmentation methods (p > 0.176). However, there was an effect of method for both volumetric and voxel wise agreement of the segmentations (both p < 0.001). Highest volumetric and voxel wise agreement was obtained with Xinapse JIM (ICC absolute agreement = 0.940 and median SI = 0.962).ConclusionAlthough MUCCA is highly reproducible within a scanner for each individual measurement method, MUCCA differs between scanners and between methods. Cervical cord lesions do not affect MUCCA measurement performance.