Simon K. Warfield

Bio: Simon K. Warfield is an academic researcher from Boston Children's Hospital. The author has contributed to research in topics: Segmentation & Diffusion MRI. The author has an hindex of 83, co-authored 530 publications receiving 28034 citations. Previous affiliations of Simon K. Warfield include Children's Medical Center of Dallas & Cincinnati Children's Hospital Medical Center.

Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

Abstract: Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.

Abnormal Cerebral Structure Is Present at Term in Premature Infants

Abstract: Background. Long-term studies of the outcome of very prematurely born infants have clearly documented that the majority of such infants have significant motor, cognitive, and behavioral deficits. However, there is a limited understanding of the nature of the cerebral abnormality underlying these adverse neurologic outcomes. Aim. The overall aim of this study was to define quantitatively the alterations in cerebral tissue volumes at term equivalent in a large longitudinal cohort study of very low birth weight premature infants in comparison to term-born infants by using advanced volumetric 3-dimensional magnetic resonance imaging (MRI) techniques. We also aimed to define any relationship of such perinatal lesions as white matter (WM) injury or other potentially adverse factors to the quantitative structural alterations. Additionally, we wished to identify the relationship of the structural alterations to short-term neurodevelopmental outcome. Methods. From November 1998 to December 2000, 119 consecutive premature infants admitted to the neonatal intensive care units at Christchurch Women’s Hospital (Christchurch, New Zealand) and the Royal Women’s Hospital (Melbourne, Australia) were recruited (88% of eligible) after informed parental consent to undergo an MRI scan at term equivalent. Twenty-one term-born infants across both sites were recruited also. Postacquisition advanced 3-dimensional tissue segmentation with 3-dimensional reconstruction was undertaken to estimate volumes of cerebral tissues: gray matter (GM; cortical and deep nuclear structures), WM (myelinated and unmyelinated), and cerebrospinal fluid (CSF). Results. In comparison to the term-born infants, the premature infants at term demonstrated prominent reductions in cerebral cortical GM volume (premature infants [mean ± SD]: 178 ± 41 mL; term infants: 227 ± 26 mL) and in deep nuclear GM volume (premature infants: 10.8 ± 4.1 mL; term infants: 13.8 ± 5.2 mL) and an increase in CSF volume (premature infants: 45.6 ± 22.1 mL; term infants: 28.9 ± 16 mL). The major predictors of altered cerebral volumes were gestational age at birth and the presence of cerebral WM injury. Infants with significantly reduced cortical GM and deep nuclear GM volumes and increased CSF volume volumes exhibited moderate to severe neurodevelopmental disability at 1 year of age. Conclusions. This MRI study of prematurely born infants further defines the nature of quantitative cerebral structural abnormalities present as early as term equivalent. The abnormalities particularly involve cerebral neuronal regions including both cortex and deep nuclear structures. The pattern of cerebral alterations is related most significantly to the degree of immaturity at birth and to concomitant WM injury. The alterations are followed by abnormal short-term neurodevelopmental outcome.

Early Experience Alters Brain Function and Structure

Abstract: Objective. To investigate the effects of early experience on brain function and structure. Methods. A randomized clinical trial tested the neu- rodevelopmental effectiveness of the Newborn Individ- ualized Developmental Care and Assessment Program (NIDCAP). Thirty preterm infants, 28 to 33 weeks' ges- tational age (GA) at birth and free of known develop- mental risk factors, participated in the trial. NIDCAP was initiated within 72 hours of intensive care unit admission and continued to the age of 2 weeks, corrected for pre- maturity. Control (14) and experimental (16) infants were assessed at 2 weeks' and 9 months' corrected age on health status, growth, and neurobehavior, and at 2 weeks' corrected age additionally on electroencephalogram spec- tral coherence, magnetic resonance diffusion tensor im- aging, and measurements of transverse relaxation time. Results. The groups were medically and demograph- ically comparable before as well as after the treatment. However, the experimental group showed significantly better neurobehavioral functioning, increased coherence between frontal and a broad spectrum of mainly occipital brain regions, and higher relative anisotropy in left in- ternal capsule, with a trend for right internal capsule and frontal white matter. Transverse relaxation time showed no difference. Behavioral function was improved also at 9 months' corrected age. The relationship among the 3 neurodevelopmental domains was significant. The re- sults indicated consistently better function and more ma- ture fiber structure for experimental infants compared with their controls. Conclusions. This is the first in vivo evidence of en- hanced brain function and structure due to the NIDCAP. The study demonstrates that quality of experience before term may influence brain development significantly. Pe- diatrics 2004;113:846 - 857; preterm infants, NIDCAP, neu- robehavior, spectral coherence, diffusion tensor imaging, transverse relaxation time, Bayley Scales of Infant Devel- opment, APIB.

Improved watershed transform for medical image segmentation using prior information

Abstract: The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or