The evaluation of head malformations plays an essential role in the early diagnosis, the decision to perform surgery and the assessment of the surgical outcome of patients with craniosynostosis. Clinicians rely on two metrics to evaluate the head shape: head circumference (HC) and cephalic index (CI). However, they present a high inter-observer variability and they do not take into account the location of the head abnormalities. In this study, we present an automated framework to objectively quantify the head malformations, HC, and CI from three-dimensional (3D) photography, a radiation-free, fast and non-invasive imaging modality. Our method automatically extracts the head shape using a set of landmarks identified by registering the head surface of a patient to a reference template in which the position of the landmarks is known. Then, we quantify head malformations as the local distances between the patient’s head and its closest normal from a normative statistical head shape multi-atlas. We calculated cranial malformations, HC, and CI for 28 patients with craniosynostosis, and we compared them with those computed from the normative population. Malformation differences between the two populations were statistically significant (p<0.05) at the head regions with abnormal development due to suture fusion. We also trained a support vector machine classifier using the malformations calculated and we obtained an improved accuracy of 91.03% in the detection of craniosynostosis, compared to 78.21% obtained with HC or CI. This method has the potential to assist in the longitudinal evaluation of cranial malformations after surgical treatment of craniosynostosis.
Functional analysis of the placenta is important to analyze and understand its role in fetal growth and development. BOLD MR is a non-invasive technique that has been extensively used for functional analysis of the brain. During the last years, several studies have shown that this dynamic image modality is also useful to extract functional information of the placenta. We propose in this paper a method to track the placenta from a sequence of BOLD MR images acquired under normoxia and hyperoxia conditions with the goal of quantifying how the placenta adapts to oxygenation changes. The method is based on a spatiotemporal transformation model that ensures temporal coherence of the tracked structures. The method was initially applied to four patients with healthy pregnancies. An average MR signal increase of 16.96±8.39% during hyperoxia was observed. These automated results are in line with state-of-the-art reports using time-consuming manual segmentations subject to inter-observer errors.
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