Liver segmentation: Practical tips.

The liver segmentation system, described by Couinaud, is based on the identification of the three hepatic veins and the plane passing by the portal vein bifurcation. Nowadays, Couinaud's description is the most widely used classification since it is better suited for surgery and more accurate for the localisation and monitoring of intra-parenchymal lesions. Knowledge of the anatomy of the portal and venous system is therefore essential, as is knowledge of the variants resulting from changes occurring during the embryological development of the vitelline and umbilical veins. In this paper, the authors propose a straightforward systematisation of the liver in six steps using several additional anatomical points of reference. These points of reference are simple and quickly identifiable in any radiological examination with section imaging, in order to avoid any mistakes in daily practice. In fact, accurate description impacts on many diagnostic and therapeutic applications in interventional radiology and surgery. This description will allow better preparation for biopsy, portal vein embolisation, transjugular intrahepatic portosystemic shunt, tumour resection or partial hepatectomy for transplantation. Such advance planning will reduce intra- and postoperative difficulties and complications.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

Brain Surface Segmentation of Magnetic Resonance Images of the Fetus

In this work we present a method for the image analysisof Magnetic Resonance Imaging (MRI) of fetuses. Our goalis to segment the brain surface from multiple volumes(axial, coronal and sagittal acquisitions) of a fetus. Tothis end we propose a two-step approach: first, a FiniteGaussian Mixture Model (FGMM) will segment the image into3 classes: brain, non-brain and mixture voxels. Second, aMarkov Random Field scheme will be applied tore-distribute mixture voxels into either brain ornon-brain tissue. Our main contributions are an adaptedenergy computation and an extended neighborhood frommultiple volumes in the MRF step. Preliminary results onfour fetuses of different gestational ages will be shown.

Wage cuts and managers' empathy: How a positive emotion can contribute to positive organizational ethics in difficult times

Using the lens of positive organizational ethics, we theorized that empathy affects decisions in ethical dilemmas that concern the well-being of not only the organization but also other stakeholders. We hypothesized and found that empathetic managers were less likely to comply with requests by an authority figure to cut the wages of their employees than were non-empathetic managers. However, when an authority figure requested to hold wages constant, empathy did not affect wage cut decisions. These findings imply that empathy can serve as a safeguard for ethical decision making in organizations during trying times without generally undermining organizational effectiveness. We conclude by discussing the implications of our research.

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.