Method and system for the automatic recognition of lesions in a set of breast magnetic resonance images

A method of identification of potential lesions of a breast from tomographic image datasets of a chest region of a patient, the- datasets comprising a plurality of voxels (2) each having an intensity value, the images including a region of interest (10) which comprises at least one breast (6). The method comprises the steps of: acquiring a set of images after the administration of a contrast agent to the patient; normalizing (254) the intensity of voxels (2) belonging to the region of interest (10) of the acquired images according to at least one normalization factor; classifying (255) each of the normalized voxels (2) on the basis of a classification criterion, in such a way as to identify regions (40) representing potential lesions. The method is characterized in that the normalization factor is based on normalization voxels (2) corresponding to an anatomical structure (34), the normalization voxels (2) having intensity values enhanced due to the administration of the contrast agent

Validation of an elastic registration technique to estimate anatomical lung modification in Non-Small-Cell Lung Cancer Tomotherapy

<p>Abstract</p> <p>Background</p> <p>The study of lung parenchyma anatomical modification is useful to estimate dose discrepancies during the radiation treatment of Non-Small-Cell Lung Cancer (NSCLC) patients. We propose and validate a method, based on free-form deformation and mutual information, to elastically register planning kVCT with daily MVCT images, to estimate lung parenchyma modification during Tomotherapy.</p> <p>Methods</p> <p>We analyzed 15 registrations between the planning kVCT and 3 MVCT images for each of the 5 NSCLC patients. Image registration accuracy was evaluated by visual inspection and, quantitatively, by Correlation Coefficients (CC) and Target Registration Errors (TRE). Finally, a lung volume correspondence analysis was performed to specifically evaluate registration accuracy in lungs.</p> <p>Results</p> <p>Results showed that elastic registration was always satisfactory, both qualitatively and quantitatively: TRE after elastic registration (average value of 3.6 mm) remained comparable and often smaller than voxel resolution. Lung volume variations were well estimated by elastic registration (average volume and centroid errors of 1.78% and 0.87 mm, respectively).</p> <p>Conclusions</p> <p>Our results demonstrate that this method is able to estimate lung deformations in thorax MVCT, with an accuracy within 3.6 mm comparable or smaller than the voxel dimension of the kVCT and MVCT images. It could be used to estimate lung parenchyma dose variations in thoracic Tomotherapy.</p

Implementación en paralelo de un modelo de transmisión sináptica sobre un cluster heterogéneo

En este trabajo se presentan los primeros resultados obtenidos por el área de informática de la Univ. Nac. de Gral. Sarmiento, en su proyecto de diseño e implementación de un cluster Linux, de tipo heterogéneo. Estos resultados se aplicaron en la implementación en paralelo de una aplicación desarrollada para el proyecto de investigación de Sistemas Complejos del área de física de dicha Institución. Como resultado de esta primera etapa se midió la capacidad de cómputo del cluster, obteniendo coeficientes que dependen de las características físicas de cada nodo y que permiten implementar aplicaciones paralelas, de manera estática, aprovechando al máximo el sistema de cómputo. El tiempo de ejecución de esta aplicación es de unas 20 hs. en su versión serial más simple y en las computadoras más rápidas, tiempo que irá creciendo en futuras aplicaciones. Su implementación en paralelo, utilizando dichos coeficientes, permitió disminuir un 60% el tiempo de ejecución. Mediante una implementación en paralelo, con asignación dinámica de la carga, se estudió el comportamiento del sistema al momento de balancear cómputo y comunicaciones, pensando continuar en el futuro mejorando este comportamiento e integrando este cluster a una colección de clusters heterogéneos interconectados a través de Internet.Eje: Procesamiento distribuido y paralelo (PDP)Red de Universidades con Carreras en Informática (RedUNCI

Implementación en paralelo de un modelo de transmisión sináptica sobre un cluster heterogéneo

En este trabajo se presentan los primeros resultados obtenidos por el área de informática de la Univ. Nac. de Gral. Sarmiento, en su proyecto de diseño e implementación de un cluster Linux, de tipo heterogéneo. Estos resultados se aplicaron en la implementación en paralelo de una aplicación desarrollada para el proyecto de investigación de Sistemas Complejos del área de física de dicha Institución. Como resultado de esta primera etapa se midió la capacidad de cómputo del cluster, obteniendo coeficientes que dependen de las características físicas de cada nodo y que permiten implementar aplicaciones paralelas, de manera estática, aprovechando al máximo el sistema de cómputo. El tiempo de ejecución de esta aplicación es de unas 20 hs. en su versión serial más simple y en las computadoras más rápidas, tiempo que irá creciendo en futuras aplicaciones. Su implementación en paralelo, utilizando dichos coeficientes, permitió disminuir un 60% el tiempo de ejecución. Mediante una implementación en paralelo, con asignación dinámica de la carga, se estudió el comportamiento del sistema al momento de balancear cómputo y comunicaciones, pensando continuar en el futuro mejorando este comportamiento e integrando este cluster a una colección de clusters heterogéneos interconectados a través de Internet.Eje: Procesamiento distribuido y paralelo (PDP)Red de Universidades con Carreras en Informática (RedUNCI

Performance of a fully automatic lesion detection system for breast DCE-MRI

PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice

Intra-operative 5-aminolevulinic acid (ALA)-induced fluorescence of medulloblastoma: phenotypic variability and CD133(+) expression according to different fluorescence patterns.

5-Aminolevulinic acid (5-ALA) fluorescence has been proved advantageous in glioma surgery. Conflicting results have been reported by few studies published in literature about intra-operative 5-ALA-induced fluorescence of medulloblastoma (MDB). The aim of this study is to verify if these conflicting results could be explained by intra-tumoral histological and phenotypic differences. In the present case of a 45-year-old patient affected by a cerebellar MDB, histological analysis of cell phenotype and 5-ALA and CD133 correlation were performed in multiple samples according to different fluorescence patterns. Intra-operatively, the tumor appeared unevenly fluorescent under blue-violet light. Histologically, 5-ALA-intense biopsies from inner areas were characterized by a significant amount of cancer cells, whereas 5-ALA faint regions from peripheral areas displayed normal cerebellar features, with MDB cells infiltrating healthy tissues. Presenting our findings, we show the correlation between different 5-ALA fluorescence patterns of medulloblastoma with specific histological and phenotypical features. Thus, we hypothesize that a distinct relationship between CD133 expression and fluorescence accumulation presented in our study could partially explain the divergent results published in literatur

A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images

Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction. We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results. © 2009 SPIE

A fully automatic algorithm for segmentation of the breasts in DCE-MR images

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79±0.09, recall=0.95± 0.02, precision=0.82 ± 0.1)