Atlas-based segmentation of pathological brain MR images

We propose a method for brain atlas deformation inpresence of large space-occupying tumors, based on an apriori model of lesion growth that assumes radialexpansion of the lesion from its starting point. First,an affine registration brings the atlas and the patientinto global correspondence. Then, the seeding of asynthetic tumor into the brain atlas provides a templatefor the lesion. Finally, the seeded atlas is deformed,combining a method derived from optical flow principlesand a model of lesion growth (MLG). Results show that themethod can be applied to the automatic segmentation ofstructures and substructures in brains with grossdeformation, with important medical applications inneurosurgery, radiosurgery and radiotherapy.

Automated Segmentation of Cerebral Vasculature with Aneurysms in 3DRA and TOF-MRA using Geodesic Active Regions: an Evaluation Study

Purpose: To evaluate the suitability of an improved version of an automatic segmentation method based on geodesic active regions (GAR) for segmenting cerebral vasculature with aneurysms from 3D X-ray reconstruc-tion angiography (3DRA) and time of °ight magnetic resonance angiography (TOF-MRA) images available in the clinical routine.Methods: Three aspects of the GAR method have been improved: execution time, robustness to variability in imaging protocols and robustness to variability in image spatial resolutions. The improved GAR was retrospectively evaluated on images from patients containing intracranial aneurysms in the area of the Circle of Willis and imaged with two modalities: 3DRA and TOF-MRA. Images were obtained from two clinical centers, each using di®erent imaging equipment. Evaluation included qualitative and quantitative analyses ofthe segmentation results on 20 images from 10 patients. The gold standard was built from 660 cross-sections (33 per image) of vessels and aneurysms, manually measured by interventional neuroradiologists. GAR has also been compared to an interactive segmentation method: iso-intensity surface extraction (ISE). In addition, since patients had been imaged with the two modalities, we performed an inter-modality agreement analysis with respect to both the manual measurements and each of the two segmentation methods. Results: Both GAR and ISE di®ered from the gold standard within acceptable limits compared to the imaging resolution. GAR (ISE, respectively) had an average accuracy of 0.20 (0.24) mm for 3DRA and 0.27 (0.30) mm for TOF-MRA, and had a repeatability of 0.05 (0.20) mm. Compared to ISE, GAR had a lower qualitative error in the vessel region and a lower quantitative error in the aneurysm region. The repeatabilityof GAR was superior to manual measurements and ISE. The inter-modality agreement was similar between GAR and the manual measurements. Conclusions: The improved GAR method outperformed ISE qualitatively as well as quantitatively and is suitable for segmenting 3DRA and TOF-MRA images from clinical routine.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.

Introdução à Geometria Fractal

No passado, a Matemática esteve, em grande parte, preocupada com conjuntos e funções que podem ser estudados através dos métodos clássicos de cálculo1. Por exemplo, na geometria, Havia o hábito de descrever os objectos através de formas regulares: rectas, circunferências, cones etc. Mas, será que uma nuvem é formada por esferas, uma montanha por cones e continentes por circunferências? Existem alguns objectos na natureza, nas ciências em geral e na matemática, em particular (conjuntos, funções), que não são suficientemente "lisos" e que tendiam a ser ignorados e rotulados como “patológicos” . Tais objectos foram considerados como curiosidades, e assim, estudados e analisados por alguns investigadores ao longo dos tempos. Porém, em 1960, Benoit B. Mandelbrot2, trouxe essa matéria à agenda matemática da actualidade, apresentando uma fundamentação coerente do que seriam essas "não-formas". Refazendo alguns estudos nessa área e conhecendo ideias de outros autores apresentou estudos sobre fractais criando assim a teoria dos fractais ou a geometria fractal. Os fractais caracterizam-se por terem uma aparência complexa e confusa, em certos casos, mas quando olhados matematicamente, sua análise denota figuras que apresentam regularidades e comportamentos curiosos, como o de se assemelharem a elas mesmas quando observadas a diferentes escalas, por exemplo. A geometria fractal é portanto o ramo da Matemática que estuda as propriedades dos fractais. Descreve muitas situações que não podem ser explicadas facilmente pela Geometria Euclidiana. A geometria fractal descreve taambém como os fractais podem ser aplicados na ciência, tecnologia, arte, etc., sobretudo com recurso computadores. A geometria fractal ainda não fez a sua entrada nos programas dematemática no sistema educativo cabo-verdiano, sendo portanto, pouco conhecida nesse meio. Assim escolhemos essa geometria como tema do nosso trabalho, cujo objectivo geral é divulgar o mundo dos fractais e as suas aplicações, na educação. Aprofundar os conhecimentos sobre a geometria fractal e suas aplicações práticas e no ensino, integrar os conhecimentos de Álgebra Linear, Geometria e Topologia adquiridos no curso e aplicar os fractais ao estudo das sucessões (progressões geométricas) são os objectivos específicos. A partir destes objectivos surgiram as nossas questões de investigação, que tentamos responder ao longo do estudo: 1. Como se fundamenta a geometria fractal? 2. Quais são as principais aplicações? 3. Como aplicar os fractais no ensino secundário (sucessões), de modo a tornar o ensino de matemática mais interessante e motivador? Tais são as questões para as quais procuramos uma resposta ao longo do desenvolvimento do projecto.

Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease.

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.

A natureza fractal de ácidos húmicos

Dentre as ferramentas usadas para descrever a estrutura ramificada ou a superfície rugosa e distorcida de ácidos húmicos (AH), a geometria fractal aparece como uma das mais adequadas para explicar a conformação de partículas húmicas (agregados moleculares). Do ponto de vista experimental, a dimensão fractal (D) de sistemas naturais pode ser determinada a partir do monitoramento da luz transmitida, não espalhada e não absorvida (turbidimetria 'τ'). A presença de fractais implica que o sistema pode ser decomposto em partes, em que cada uma, subseqüentemente, é cópia do todo. A determinação do valor 'D' dessas partículas foi conseguida pela utilização de turbidimetria, em que suspensões de AH-comercial e de AH-Espodossolo foram analisadas por espectrofotometria UV-Vis. O fundamento matemático utilizado foi a lei de potência τ ∝ λβ, em que β < 3 indica a presença de fractal de massa (Dm); 3 < β < 4 indica fractal de superfície (Ds), e β ≅ 3 indica não-fractal (NF). A declividade das retas (β) por meio do gráfico (logτ vs logλ) permitiu a obtenção de 'D'. Segundo os resultados, partículas de AH em suspensões aquosas diluídas formam estruturas fractais, cuja geometria pode ser caracterizada por meio de turbidimetria. Entretanto, a faixa de comprimento de onda usada (400 a 550 nm) ainda é pequena para se afirmar sobre a natureza fractal de AH e determinar suas dimensões fractais com precisão.

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.

Variabilidade espacial da agregação do solo avaliada pela geometria fractal e geoestatística

Este trabalho teve por objetivo explorar a aplicabilidade da teoria de fractais no estudo da variabilidade espacial em agregação de solo. A geometria de fractais tem sido proposta como um modelo para a distribuição de tamanho de partículas. A distribuição do tamanho de agregados do solo, expressos em termos de massa, é apresentada. Os parâmetros do modelo, tais como: a dimensão fractal D, medida representativa da fragmentação do solo (quanto maior seu valor, maior a fragmentação), e o tamanho do maior agregado R L foram definidos como ferramentas descritivas para a agregação do solo. Os agregados foram coletados em uma profundidade de 0-10 cm de um Latossolo Vermelho distrófico típico álico textura argilosa, em Angatuba, São Paulo. Uma grade regular de 100 x 100 m foi usada e a amostragem realizada em 76 pontos nos quais se determinou a distribuição de agregados por via úmida, usando água, álcool e benzeno como pré-tratamentos. Pelo exame de semivariogramas, constatou-se a ocorrência de dependência espacial. A krigagem ordinária foi usada como interpolador e mapas de contorno mostraram-se de grande utilidade na descrição da variabilidade espacial de agregação do solo.