M. Bach Cuadra

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour-based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; instead, they are segmented using the dense deformation field computed from the registration of the atlas structures with distinct boundaries. This approach results in robust and accurate segmentation of the lymph node regions even in the presence of significant anatomical variations between the atlas-image and the patients image to be segmented. We also present a quantitative evaluation of lymph node regions segmentation using various statistical as well as geometrical metrics: sensitivity, specificity, dice similarity coefficient and Hausdorff distance. A comparison of the proposed method with two other state of the art methods is presented. The robustness of the proposed method to the atlas selection, in segmenting the lymph node regions, is also evaluated.

Atlas-Based Segmentation of Pathological Brains Using a Model of Tumor Growth

We propose a method for brain atlas deformation in presence of large space-occupying tumors or lesions, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its central point. Atlas-based methods have been of limited use for segmenting brains that have been drastically altered by the presence of large space-occupying lesions. Our approach involves four steps. First, an affine registration brings the atlas and the patient into global correspondence. Secondly, a local registration warps the atlas onto the patient volume. 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 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.

Validation of Tissue Modelization and Classification Techniques in T1-Weighted MR Brain Images

We propose a deep study on tissue modelization and classification Techniques on T1-weighted MR images. Three approaches have been taken into account to perform this validation study. Two of them are based on Finite Gaussian Mixture (FGM) model. The first one consists only in pure Gaussian distributions (FGM-EM). The second one uses a different model for partial volume (PV) (FGM-GA). The third one is based on a Hidden Markov Random Field (HMRF) model. All methods have been tested on a Digital Brain Phantom image considered as the ground truth. Noise and intensity non-uniformities have been added to simulate real image conditions. Also the effect of an anisotropic filter is considered. Results demonstrate that methods relying in both intensity and spatial information are in general more robust to noise and inhomogeneities. However, in some cases there is no significant differences between all presented methods.

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.

An efficient segmentation method for ultrasound images based on a semi-supervised approach and patch-based features

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm is based on a semi-supervised approach (user labels) and the use of image patches as data features. We also consider the Pearson distance between patches, which has been shown to be robust w.r.t speckle noise present in ultrasound images. Our results on phantom and clinical data show a very high similarity agreement with the ground truth provided by a medical expert.

Pretherapeutic functional neuroimaging predicts tremor arrest after thalamotomy

Essential tremor (ET) represents the most common movement disorder. Drug‐resistant ET can benefit from standard stereotactic procedures (deep brain stimulation or radiofrequency thalamotomy) or alternatively minimally invasive high‐focused ultrasound or radiosurgery. All aim at same target, thalamic ventro‐intermediate nucleus (Vim).

How can physicians quantify brain degeneration

Life expectation is increasing every year. Along with this aging population, the risk of neurological diseases (e.g., dementia)1 is considerably increasing2 as well. Such disorders of the human nervous system affect the patient from both a physical and a social point of view, as in the case of Alzheimer’s disease, one of the most known brain disorders (Mazziotta et al. 2000).