Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Model-driven segmentation of X-ray left ventricular angiograms

X-ray left ventricular (LV) angiography is an important imaging modality to assess cardiac function. Using a contrast fluid a 2D projection of the heart is obtained. In current clinical practice cardiac function is analyzed by drawing two contours manually: one in the end diastolic (ED) phase and one in the end systolic (ES) phase. From the contours the LV volumes in these phases are calculated and the patient__s ejection fraction is assessed. Drawing these contours manually is a cumbersome and time-consuming task for a medical doctor. Furthermore, manual drawing introduces inter- and intra-observer variabilities. The focus of the research presented in this thesis was to automate the process of contour drawing in X-ray LV angiography. The developed method is based on Active Appearance Models. These statistical models, in which the cardiac shape and the cardiac appearance are modeled, have proven to be able to mimic the drawing behavior of an expert cardiologist. The clinical parameters, as determined by the automated method, showed a similar degree of accuracy as when determined by an expert. Furthermore, the required time for patient analysis was reduced considerably and the inter- and intra-observer variabilities were structurally decreased.UBL - phd migration 201

Angiocardiología por rayos X

La radiología permite obtener una películaradiográfica de la imagen de una parte delcuerpo humano, por su exposición a los rayosX. Cuando la radiación X atraviesa el objetobajo estudio, sufre una atenuación que depende dela densidad y el espesor del objeto. Los rayos atenuadosllegan a un a un receptor que puede ser la película fotográfica,produciendo así una imagen cuyo contraste facilitaráel diagnóstico médico. La angiografía es un procedimientoradiológico usado para observar el flujo de sangre,en cualquier órgano del cuerpo. Bajo este procedimientodestacan la angiografía cardiaca para observar las arteriascoronarias, la angiografía vascular para estudiar la irrigacióndel cerebro, y la ventriculografía, para observar la cavidadventricular. En el presente artículo, se presentan unconjunto de técnicas desarrolladas para el procesamientode imágenes adquiridas durante procedimientos de angiografíacardiaca

Evaluación de técnicas para la detección de la cavidad ventricular izquierda en imágenes de angiografía cardiaca por rayos X

En este trabajo, se compara el desempeño de tres (3) técnicas diseñadas para la detección de la cavidad ventricular izquierda en imágenes de angiografía cardiaca, con el fin de obtener la información necesaria para cuantificar la función ventricular. Las técnicas se basan en a) aproximación lineal (AL), b) modelos de cuerpos deformables (snake), y c) relaciones funcionales y técnicas de agrupamiento (RFTA). La comparación se establece tomando como referencia los contornos trazados por especialistas cardiólogos que definen la cavidad ventricular, y siguiendo una metodología que permite cuantificar la diferencia entre la forma final obtenida por cada método propuesto y la forma real trazada por el especialista. Los resultados comprueban que la técnica basada en modelos de cuerpos deformables es más robusta frente a cambios topológicos tales como suavidad y curvatura presentes en la forma ventricular izquierda en comparación con las otras dos técnicas propuestas. Sin embargo, la técnica basada en snake, al igual que la basada en AL, necesita un conjunto de puntos iniciales establecidos de forma manual cerca de la forma a extraer, a diferencia de la técnica de RFTA, la cual extrae la forma ventricular de manera automática

Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network

ABSTRACT We developed a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN). The MTANN is a trainable highly nonlinear filter that can be trained by using input chest radiographs and the corresponding teacher images. We used either the soft-tissue image or the bone image obtained by use of a dual-energy subtraction technique as the teacher image for suppression of ribs in chest radiographs. When the soft-tissue images were used as the teacher images, the MTANN directly produced a "soft-tissue-image-like" image where the contrast of ribs was suppressed. When the bone images were used as the teacher images, the MTANN was able to produce a "bone-image-like" image, and then was subtracted from the corresponding chest radiograph to produce a bone-subtracted image where ribs are suppressed. Thus, the two kinds of rib-suppressed images, i.e., the soft-tissue-image-like image and the bone-subtracted image, could be produced by use of the MTANNs trained with two different teacher images. We applied each of the two trained MTANNs to non-training chest radiographs to investigate the difference between the processed images. The results showed that the contrast of ribs in chest radiographs almost disappeared, and was reduced to less than 10% in both processed images. The contrast of ribs was reduced slightly better in the soft-tissue-image-like images than in the bone-subtracted images, whereas soft-tissue opacities such as lung vessels and nodules were maintained better in the bone-subtracted images. Therefore, the use of the bone images as the teacher images for training the MTANN has produced better rib-suppressed images where soft-tissue opacities were substantially maintained. A method for rib suppression using the MTANN would be useful for radiologists as well as CAD schemes in detection of lung diseases such as nodules in chest radiographs

Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data

We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.Gustavo Carneiro and Jacinto C. Nasciment

Progress Report No. 16

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1979 to 30 June 1980