RESUMEN

En este trabajo se presenta un nuevo conjunto de indicadores de severidad que combinan diversos rasgos craneales para cuantificar las craneosinostosis aisladas de tipo sagital y metópica. La utilidad de los indicadores se evaluó examinando las tomografías computarizadas del cráneo de un grupo de infantes afectados por craneosinostosis aislada y un grupo de infantes no afectados. La base de datos contiene estudios de 90 pacientes con craneosinostosis sagital, 40 con craneosinostosis metópica y 60 pacientes no afectados. Los indicadores de severidad se obtienen a partir de un conjunto de indices de severidad por medio de un método estadístico de regresión logística regularizada conocido como red elástica. Los índices de severidad son medidas univariadas de forma que se calculan a partir de tres planos de análisis. Los planos se estiman a partir de referencias anatómicas cerebrales radiológicamente identificables. El desempeño de los indicadores se midió estimando el grado de separación lineal (GSL), que cuantifica la capacidad de un indicador para distinguir cráneos sagitales o metópicos de cráneos no afectados. Los indicadores de severidad propuestos alcanzan un GSL del 95.83% y 98.9% en las poblaciones sagitales vs. controles y metópicos vs. controles, respectivamente. Los resultados obtenidos en este trabajo sugieren que es posible construir indicadores multivariables de severidad que son clínicamente reproducibles y cuantifican efectivamente aspectos de la morfología craneal codificada por medio de un conjunto de índices de severidad.

This work develops a new set of severity scores that combine several cranial features in order to quantify sagittal and metopic craniosynostosis. Computed tomography head scans were obtained from 90 children affected with single-suture sagittal synostosis, 40 children with single-suture metopic synostosis, and 60 age-matched nonsynostotic controls. Tridimensional reconstructions of the skull were used to trace image analysis planes defined in terms of skull-base plane and internal landmarks. For each patient, a new set of descriptive measures or severity indices of skull shape malformation were computed. A statistical classification approach (regularized logistic regression) was used for combining individual severity indices into summarizing severity scores. The linear separation index that measures the ability of a classification function to separate the affected (sagittal or metopic) and nonsynostotic populations was used to evaluate the severity scores. The proposed scores are sensitive measures of the calvarial malformation that achieve linear separation indices of 95.83% and 98.9% for sagittal vs. control and metopic vs. control populations, respectively. As opposed to individual severity indices, the summarizing severity scores encapsulate a number of distinctive calvarial features associated with sagittal and metopic synostoses crania. The proposed scores enable quantitative analysis in clinical settings of skull features observed in isolated sagittal and metopic synostoses that may not be accurately detected by separate analysis of individual severity indices.

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This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The event-induced power of the EEG data recorded at 46weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique.

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Single-suture craniosynostosis is a condition of the sutures of the infant's skull that causes major craniofacial deformities and is associated with an increased risk of cognitive deficits and learning/language disabilities. In this paper we adapt to classification of synostostic head shapes a Bayesian methodology that overcomes the limitations of our previously published shape representation and classification techniques. We evaluate our approach in a series of large-scale experiments and show performance superior to those of standard approaches such as Fourier descriptors, cranial spectrum, and Euclidian-distance-based analyses.

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Craniosynostosis is a serious and common pediatric disease caused by the premature fusion of sutures of the skull. Although studies have shown an increase in risk for cognitive deficits in patients with isolated craniosynostosis, the causal basis for this association is still unclear. It is hypothesized that an abnormally shaped skull produces a secondary deformation of the brain that results in the disruption of normal neuropsychological development. In this paper, we conduct a comparative analysis of our newly developed shape descriptors in an attempt to understand the impact of skull deformations on neurobehavior. In particular, we show that our scaphocephaly severity indices and symbolic shape signatures are predictive of mental ability and psychomotor functions, respectively, which suggests the possibility that secondary deformation could influence neuro-developmental status.

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RESUMEN

Craniosynostosis is a serious condition of childhood, caused by the early fusion of the sutures of the skull. The resulting abnormal skull development can lead to severe deformities, increased intra-cranial pressure, as well as vision, hearing and breathing problems. In this work we develop a novel approach to accurately classify deformations caused by metopic and isolated sagittal synostosis. Our method combines a novel set of symbolic shape descriptors and off-the-shelf classification tools to model morphological variations that characterize the synostotic skull. We demonstrate the efficacy of our methodology in a series of large-scale classification experiments that contrast the performance of our proposed symbolic descriptors to those of traditional numeric descriptors, such as clinical severity indices, Fourier-based descriptors and cranial image quantifications.

RESUMEN

The relation between morphological gray-level connected operators and segmentation algorithms based on region merging/classification strategies has been pointed out several times in the literature. However, to the best of our knowledge, the formal relation between them has not been established. This paper presents the link between the two domains based on the observation that both connected operators and segmentation algorithms share a key mechanism: they simultaneously operate on images and on partitions, and therefore they can be described as operations on a joint image-partition model. As a result, we analyze both segmentation algorithms and connected operators by defining operators on complete product lattices, that explicitly model gray-level and partition attributes. In the first place, starting with a complete lattice of partitions, we initially define the concept of the segmentation model as a mapping in a product lattice, whose elements are three-tuples consisting of a partition, an image that models the partition attributes, and an image that represents the gray-level model associated to the segmentation. Then, assuming a conditional ordering relation, we show that any region merging/classification segmentation algorithm can be defined as an extensive operator in such a complete product lattice, in the second place, we proposed a very similar lattice-based extended representation of gray-level functions in the context of connected operators, that highlights the mathematical analogy with segmentation algorithms, but in which the ordering relation is different. We use this framework to show that every region merging/classification segmentation algorithm indeed corresponds to a connected operator. While this result provides an explanation to previous work in the area, it also opens possibilities for further analysis in the two domains. From this perspective, we additionally study some theoretical properties of a general region merging segmentation algorithm.