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Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity.To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI.Retrospective.One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics.A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence.Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image.Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value lt; 0.05 was considered statistically significant.Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P = 0.4358, body: P = 0.0992, tail: P = 0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index.The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity.4 TECHNICAL EFFICACY STAGE: 3.

INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS.METHODS: The DLC neural network was trained on 589 HN cancer patients. DLC was compared to ABAS by providing each method with an independent validation cohort of 104 patients, which had also been manually contoured. For each of the 22 OAR contours - glandular, upper digestive tract and central nervous system (CNS)-related structures - the dice similarity coefficient (DICE), and absolute mean and max dose differences (|Δmean-dose| and |Δmax-dose|) performance measures were obtained. For a subset of 7 OARs, an evaluation of contouring time, inter-observer variation and subjective judgement was performed.RESULTS: DLC resulted in equal or significantly improved quantitative performance measures in 19 out of 22 OARs, compared to the ABAS (DICE/|Δmean dose|/|Δmax dose|: 0.59/4.2/4.1 Gy (ABAS); 0.74/1.1/0.8 Gy (DLC)). The improvements were mainly for the glandular and upper digestive tract OARs. DLC significantly reduced the delineation time for the inexperienced observer. The subjective evaluation showed that DLC contours were more often preferable to the ABAS contours overall, were considered to be more precise, and more often confused with manual contours. Manual contours still outperformed both DLC and ABAS; however, DLC results were within or bordering the inter-observer variability for the manual edited contours in this cohort.CONCLUSION: The DLC, trained on a large HN cancer patient cohort, outperformed the ABAS for the majority of HN OARs.

Purpose Methods Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed. The purpose of this study is to propose a method (inspired by the Turing Test) for providing contour quality measures that directly draw upon practitioners' assessments of manual and automatic contours. This approach assumes that an inability to distinguish automatically produced contours from those of clinical experts would indicate that the contours are of sufficient quality for clinical use. In turn, it is anticipated that such contours would receive less manual editing prior to being accepted for clinical use. In this study, an initial assessment of this approach is performed with radiation oncologists and therapists. Eight clinical observers were presented with thoracic organ-at-risk contours through a web interface and were asked to determine if they were automatically generated or manually delineated. The accuracy of the visual determination was assessed, and the proportion of contours for which the source was misclassified recorded. Contours of six different organs in a clinical workflow were for 20 patient cases. The time required to edit autocontours to a clinically acceptable standard was also measured, as a gold standard of clinical utility. Established quantitative measures of autocontouring performance, such as Dice similarity coefficient with respect to the original clinical contour and the misclassification rate accessed with the proposed framework, were evaluated as surrogates of the editing time measured. Results Conclusions The misclassification rates for each organ were: esophagus 30.0%, heart 22.9%, left lung 51.2%, right lung 58.5%, mediastinum envelope 43.9%, and spinal cord 46.8%. The time savings resulting from editing the autocontours compared to the standard clinical workflow were 12%, 25%, 43%, 77%, 46%, and 50%, respectively, for these organs. The median Dice similarity coefficients between the clinical contours and the autocontours were 0.46, 0.90, 0.98, 0.98, 0.94, and 0.86, respectively, for these organs. A better correspondence with time saving was observed for the misclassification rate than the quantitative contour measures explored. From this, we conclude that the inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall. Hence, task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.

Accepted manuscript, This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images., The challenge organizers would like to thank Artem Mamonov and Andrew Beers from Harvard Medical School for providing valuable support in creating the challenge scoring system on the challenge website, Kirk Smith and Tracy Nolan from University of Arkansas for Medical Sciences for data curation to TCIA, Tim Lustberg from Maastro Clinic for data collection, and AAPM for sponsoring this challenge. Of the challenge participants, Brent van der Heyden would like to thank Frank Verhaegen and Mark Podesta for valuable discussions; Bruno Oliveira would like to thank Sandro Queirós, Pedro Morais, Helena R. Torres, Jaime C. Fonseca, João Gomes-Fonseca, and João L. Vilaça for all the contributions to his work; Leonid Zamdborg would like to acknowledge Thomas M Guerrero and Edward Castillo. This project was support in part by the CPRIT (Cancer Prevention Research Institute of Texas) grant No. RP110562-P2, the National Institutes of Health Cancer Center Support (Core) grant No. CA016672 to the University of Texas MD Anderson Cancer Center, and in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government, info:eu-repo/semantics/publishedVersion

Knowledge of atrial wall thickness (AWT) has the potential to provide important information for patient stratification and the planning of interventions in atrial arrhythmias. To date, information about AWT has only been acquired in post-mortem or poor-contrast computed tomography (CT) studies, providing limited coverage and highly variable estimates of AWT. We present a novel contrast agent-free MRI sequence for imaging AWT and use it to create personalized AWT maps and a biatrial atlas. A novel black-blood phase-sensitive inversion recovery protocol was used to image ten volunteers and, as proof of concept, two atrial fibrillation patients. Both atria were manually segmented to create subject-specific AWT maps using an average of nearest neighbors approach. These were then registered non-linearly to generate an AWT atlas. AWT was 2.4 ± 0.7 and 2.7 ± 0.7 mm in the left and right atria, respectively, in good agreement with post-mortem and CT data, where available. AWT was 2.6 ± 0.7 mm in the left atrium of a patient without structural heart disease, similar to that of volunteers. In a patient with structural heart disease, the AWT was increased to 3.1 ± 1.3 mm. We successfully designed an MRI protocol to non-invasively measure AWT and create the first whole-atria AWT atlas. The atlas can be used as a reference to study alterations in thickness caused by atrial pathology. The protocol can be used to acquire personalized AWT maps in a clinical setting and assist in the treatment of atrial arrhythmias.

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a "rich club"--a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical-subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants.

Cortical maturation was studied in 65 infants between 27 and 46 wk postconception using structural and diffusion magnetic resonance imaging. Alterations in neural structure and complexity were inferred from changes in mean diffusivity and fractional anisotropy, analyzed by sampling regions of interest and also by a unique whole-cortex mapping approach. Mean diffusivity was higher in gyri than sulci and in frontal compared with occipital lobes, decreasing consistently throughout the study period. Fractional anisotropy declined until 38 wk, with initial values and rates of change higher in gyri, frontal and temporal poles, and parietal cortex; and lower in sulcal, perirolandic, and medial occipital cortex. Neuroanatomical studies and experimental diffusion–anatomic correlations strongly suggested the interpretation that cellular and synaptic complexity and density increased steadily throughout the period, whereas elongation and branching of dendrites orthogonal to cortical columns was later and faster in higher-order association cortex, proceeding rapidly before becoming undetectable after 38 wk. The rate of microstructural maturation correlated locally with cortical growth, and predicted higher neurodevelopmental test scores at 2 y of age. Cortical microstructural development was reduced in a dose-dependent fashion by longer premature exposure to the extrauterine environment, and preterm infants at term-corrected age possessed less mature cortex than term-born infants. The results are compatible with predictions of the tension theory of cortical growth and show that rapidly developing cortical microstructure is vulnerable to the effects of premature birth, suggesting a mechanism for the adverse effects of preterm delivery on cognitive function.

Medical image registration plays an increasingly important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions, and the follow-up and monitoring of patients. The primary goal of image registration is to find corresponding anatomical or functional locations in two or more images. This has many applications: registration can be applied to images from the same subject acquired by different imaging modalities (multimodal image registration) or at different time points (serial image registration). In this article, we will illustrate some of the advances that have been made over recent decades. We will discuss some of the theoretical aspects of nonrigid registration and describe methods for their implementation. Finally, we will illustrate how common problems in medical imaging, such as motion correction and image segmentation, can be solved using image registration.

Objective: Preterm infants have reduced cerebral tissue volumes in adolescence. This study addresses the question: Is reduced global brain growth in the neonatal period inevitable after premature birth, or is it associated with specific medical risk factors? Methods: Eighty-nine preterm infants at term equivalent age without focal parenchymal brain lesions were studied with 20 full-term control infants. Using a deformation-based morphometric approach, we transformed images to a reference anatomic space, and we used the transformations to calculate whole-brain volume and ventricular volume for each subject. Patterns of volume difference were correlated with clinical data. Results: Cerebral volume is not reduced compared with term born control infants ( p 0.765). Supplemental oxygen requirement at 28 postnatal days is associated with lower cerebral tissue volume at term ( p 0.001), but there were no significant differences in cerebral volumes attributable to perinatal sepsis ( p 0.515) and quantitatively defined diffuse white matter injury ( p 0.183). As expected, the ventricular system is significantly larger in preterm infants at term equivalent age compared with term control infants ( p 0.001). Interpretation: Cerebral volume is not reduced during intensive care for the majority of preterm infants, but prolonged supplemental oxygen dependence is a risk factor for early attenuation of global brain growth. The reduced cerebral tissue volume seen in adolescents born preterm does not appear to be an inevitable association of prematurity, but rather caused by either specific disease during intensive care or factors operating beyond the neonatal period. Ann Neurol 2007;62:185–192 Children and adolescents who were born with very low birth weight (VLBW) appear to have reduced total cerebral tissue volumes compared with age-matched control subjects, 1–3 and alterations in the distribution of gray matter, white matter, and ventricular cerebrospinal fluid (CSF) persist into the third decade of life. 4,5 Many of the morphological abnormalities identified in this population are associated with adverse neurodevelopmental outcomes (see Counsell and Boardman 6 for review). However, an understanding of the causes, nature, and temporal evolution of this abnormal growth pattern remains elusive. In particular, it is unclear to what degree the growth failure leading to reduced brain volume in adolescence takes place in the perinatal period or in later childhood. In a study of preterm infants at term equivalent age, brain volume was reduced compared with infants born at term, 7 but this cohort included infants with

In this paper, we present a technique that can be used to transform the motion or deformation fields defined in the coordinate system of one subject into the coordinate system of another subject. Such a transformation accounts for the differences in the coordinate systems of the two subjects due to misalignment and size/shape variation, enabling the motion or deformation of each of the subjects to be directly quantitatively and qualitatively compared. The field transformation is performed by using a nonrigid registration algorithm to determine the intersubject coordinate system mapping from the first subject to the second subject. This fixes the relationship between the coordinate systems of the two subjects, and allows us to recover the deformation/motion vectors of the second subject for each corresponding point in the first subject. Since these vectors are still aligned with the coordinate system of the second subject, the inverse of the intersubject coordinate mapping is required to transform these vectors into the coordinate system of the first subject, and we approximate this inverse using a numerical line integral method. The accuracy of our numerical inversion technique is demonstrated using a synthetic example, after which we present applications of our method to sequences of cardiac and brain images.

In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.