0000000001062417
AUTHOR
Paula Pelechano Gómez
showing 3 related works from this author
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
2021
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…
Optimización de la secuencia de difusión en RM 3T con modelo multifactorial IVIM en el estudio de la próstata
2017
Introducción El cáncer de próstata (caP) supone la neoplasia maligna más frecuentemente diagnosticada en el varón. Este tumor ha registrado un progresivo aumento de su incidencia en los últimos años, fundamentalmente por el amplio uso de la determinación del PSA (antígeno prostático específico en suero), el aumento de la esperanza de vida, y la existencia de más y mejores métodos diagnósticos. A pesar del aumento de su incidencia, actualmente muchos pacientes se consiguen diagnosticar en estadios iniciales de la enfermedad cuando el tumor está localizado y se pueden beneficiar de un aumento en las posibilidades de curación. Además, los avances en los tratamientos ofrecen nuevas opciones ter…
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Imag…
2021
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates PCa lesions, segments them, and predicts their most likely Gleason grade group (GGG). It uses 490 mp…