4 research outputs found
Automated Assessment of Aortic and Main Pulmonary Arterial Diameters using Model-Based Blood Vessel Segmentation for Predicting Chronic Thromboembolic Pulmonary Hypertension in Low-Dose CT Lung Screening
Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by obstruction of the pulmonary vasculature by residual organized thrombi. A morphological abnormality inside mediastinum of CTEPH patient is enlargement of pulmonary artery. This paper presents an automated assessment of aortic and main pulmonary arterial diameters for predicting CTEPH in low-dose CT lung screening. The distinctive feature of our method is to segment aorta and main pulmonary artery using both of prior probability and vascular direction which were estimated from mediastinal vascular region using principal curvatures of four-dimensional hyper surface. The method was applied to two datasets, 64 low-dose CT scans of lung cancer screening and 19 normal-dose CT scans of CTEPH patients through the training phase with 121 low-dose CT scans. This paper demonstrates effectiveness of our method for predicting CTEPH in low-dose CT screening
U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19
The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients
Upper airways segmentation using principal curvatures
Esta tesis propone una nueva técnica para segmentar las vÃas aéreas superiores. Esta propuesta
permite la extracción de estructuras curvilÃneas usando curvaturas principales. La propuesta
permite la extracción de éstas estructuras en imágenes 2D y 3D. Entre las principales novedades
se encuentra la propuesta de un nuevo criterio de parada en la propagación del algoritmo de
realce de contraste (operador multi-escala de tipo sombrero alto). De la misma forma, el criterio
de parada propuesto es usado para detener los algoritmos de difusión anisotrópica. Además, un
nuevo criterio es propuesto para seleccionar las curvaturas principales que conforman las
estructuras curvilÃneas, que se basa en los criterios propuestos por Steger, Deng et. al. y
Armande et. al. Además, se propone un nuevo algoritmo para realizar la supresión de nomáximos
que permite reducir la presencia de discontinuidades en el borde de las estructuras
curvilÃneas. Para extraer los bordes de las estructuras curvilÃneas, se utiliza un algoritmo de
enlace que incluye un nuevo criterio de distancia para reducir la aparición de agujeros en la
estructura final. Finalmente, con base en los resultados obtenidos, se utiliza un algoritmo
morfológico para cerrar los agujeros y se aplica un algoritmo de crecimiento de regiones para
obtener la segmentación final de las vÃas respiratorias superiores.This dissertation proposes a new approach to segment the upper airways. This proposal allows
the extraction of curvilinear structures based on the principal curvatures. The proposal
allows extracting these structures from 2D and 3D images. Among the main novelties is the
proposal of a new stopping criterion to stop the propagation of the contrast enhancement algorithm
(multiscale top-hat morphological operator). In the same way, the proposed stopping
criterion is used to stop the anisotropic diffusion algorithms. In addition, a new criterion is
proposed to select the principal curvatures that make up the curvilinear structures, which is
based on the criteria proposed by Steger, Deng et. al. and Armande et. al. Furthermore, a
new algorithm to perform the non-maximum suppression that allows reducing the presence
of discontinuities in the border of curvilinear structures is proposed. To extract the edges of
the curvilinear structures, a linking algorithm is used that includes a new distance criterion to
reduce the appearance of gaps in the final structure. Finally, based on the obtained results, a
morphological algorithm is used to close the gaps and a region growing algorithm to obtain
the final upper airways segmentation is applied.Doctor en IngenierÃaDoctorad