Craniosynostosis must often be corrected using surgery, by which the affected bone tissue is remodeled. Nowadays, surgical reconstruction relies mostly on the subjective judgement of the surgeon to best restore normal skull shape, since remodeled bone is manually placed and fixed. Slight variations can compromise the cosmetic outcome. The objective of this study was to describe and evaluate a novel workflow for patient-specific correction of craniosynostosis based on intraoperative navigation and 3D printing. The workflow was followed in five patients with craniosynostosis. Virtual surgical planning was performed, and patient-specific cutting guides and templates were designed and manufactured. These guides and templates were used to control osteotomies and bone remodeling. An intraoperative navigation system based on optical tracking made it possible to follow preoperative virtual planning in the operating room through real-time positioning and 3D visualization. Navigation accuracy was estimated using intraoperative surface scanning as the gold-standard. An average error of 0.62 mm and 0.64 mm was obtained in the remodeled frontal region and supraorbital bar, respectively. Intraoperative navigation is an accurate and reproducible technique for correction of craniosynostosis that enables optimal translation of the preoperative plan to the operating room.

This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1‐weighted magnetic resonance (MR) images. The bias field and a bias‐free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matter. Hum. Brain Mapping 22:133–144, 2004.

Microscopy images must be acquired at the optimal focal plane for the objects of interest in a scene. Although manual focusing is a standard task for a trained observer, automatic systems often fail to properly find the focal plane under different microscope imaging modalities such as bright field microscopy or phase contrast microscopy. This article assesses several autofocus algorithms applied in the study of fluorescence‐labeled tuberculosis bacteria. The goal of this work was to find the optimal algorithm in order to build an automatic real‐time system for diagnosing sputum smear samples, where both accuracy and computational time are important. We analyzed 13 focusing methods, ranging from well‐known algorithms to the most recently proposed functions. We took into consideration criteria that are inherent to the autofocus function, such as accuracy, computational cost, and robustness to noise and to illumination changes. We also analyzed the additional benefit provided by preprocessing techniques based on morphological operators and image projection profiling. © 2012 International Society for Advancement of Cytometry