Investigación

Publicación (Artículos en revistas científicas)

GoRG: Towards a GPU‐Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications

Sancho Aragón, Jaime; Sutradhar, Pallab; Chavarrías Lapastora, Miguel; Pérez-Núñez, Ángel; Salvador, Rubén; Lagares, Alfonso; Juárez Martínez, Eduardo; Sanz Álvaro, César; Rosa Olmeda, Gonzalo
Resumen:
HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation
Áreas de investigación:
Año:
2021
Tipo de publicación:
Artículos en revistas científicas
Revista:
Sensors
Volumen:
21 (12)
Número:
4091
Páginas:
1‐30
Mes:
Junio
ISSN:
1424-‐8
DOI:
10.3390/s21124091
Hits: 781