Automatic Intracranial Space Segmentation for Computed Tomography Brain Images

Craniofacial disorders are routinely diagnosed using computed tomography imaging. Corrective surgery is often performed early in life to restore the skull to a more normal shape. In order to quantitatively assess the shape change due to surgery, we present an automated method for intracranial space segmentation. The method utilizes a two-stage approach which firstly initializes the segmentation with a cascade of mathematical morphology operations. This segmentation is then refined with a level-set-based approach that ensures that low-contrast boundaries, where bone is absent, are completed smoothly. We demonstrate this method on a dataset of 43 images and show that the method produces consistent and accurate results.     

Computer Aided Preoperative Evaluation of the Residual Liver Volume Using Computed Tomography Images

Major hepatectomy causes a risk of postoperative liver dysfunction, failure, and infections like surgical site infection. Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing such surgery. The aim of this work is to develop and test a software application for evaluation of the residual function of the liver prior to the intervention of the surgeons. For this purpose, a technique for evaluation of liver volume from computed tomography (CT) images has been developed. Furthermore, the methodology algorithms were implemented and incorporated within a software tool with three basic functionalities: volume determination based on segmentation of liver from CT images, virtual tumour resection and estimation of the residual liver function and 3D visualisation. Forty-one sets of abdominal CT images consisting of different number of tomographic slice images were used to test and evaluate the proposed approach. Volumes that were obtained after manual tracing by two surgeon experts showed a relative difference of 3.5 %. The suggested methodology was encapsulated within an application with user-friendly interface that allows surgeons interactively to perform virtual tumour resection, to evaluate the relative residual liver and render the final result. Thereby, it is a tool in the surgeons’ hands that significantly facilitates their duties, saves time, and allows them to objectively evaluate the situation and take the right decisions. At the same time, the tool appears to be appropriate educational instrument for virtual training of young surgeon specialists.     

Automatic Segmentation of Pulmonary Fissures in Computed Tomography Images Using 3D Surface Features

Pulmonary interlobar fissures are important anatomic structures in human lungs and are useful in locating and classifying lung abnormalities. Automatic segmentation of fissures is a difficult task because of their low contrast and large variability. We developed a fully automatic training-free approach for fissure segmentation based on the local bending degree (LBD) and the maximum bending index (MBI). The LBD is determined by the angle between the eigenvectors of two Hessian matrices for a pair of adjacent voxels. It is used to construct a constraint to extract the candidate surfaces in three-dimensional (3D) space. The MBI is a measure to discriminate cylindrical surfaces from planar surfaces in 3D space. Our approach for segmenting fissures consists of five steps, including lung segmentation, plane-like structure enhancement, surface extraction with LBD, initial fissure identification with MBI, and fissure extension based on local plane fitting. When applying our approach to 15 chest computed tomography (CT) scans, the mean values of the positive predictive value, the sensitivity, the root–mean square (RMS) distance, and the maximal RMS are 91 %, 88 %, 1.01 ± 0.99 mm, and 11.56 mm, respectively, which suggests that our algorithm can efficiently segment fissures in chest CT scans.     

Computed Tomography and Magnetic Resonance Images of Adrenocortical Oncocytoma Cases

We present two cases of adrenocortical oncocytomas that were well-delineated on multi-detector computed tomography and magnetic resonance imaging. The images showed a well-enhanced large mass with multiple stippled calcifications in a 10-yr-old girl who was consulted due to precocious puberty. A well-enhanced solid mass with necrotic components was incidentally noticed in a 54-yr-old man. These lesions were resected and diagnosed as adrenocortical oncocytomas through immunohistochemical studies and electron microscopy. Adrenocortical oncocytomas are rare disease entities, therefore, we report these interesting, rare adrenocortical oncocytomas here with radiologic findings, and discuss differential diagnosis.

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