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

Sensors

卷:21

Rubén Salvador¹  Alfonso Lagares²  Angel Perez-Nuñez²  Jaime Sancho³  Pallab Sutradhar³  Miguel Chavarrías³  Eduardo Juárez³  Gonzalo Rosa³  César Sanz³ 

[1] CentraleSupélec, CNRS, IETR, UMR 6164, 35700 Rennes, France;

[2] Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain;

[3] Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain;

关键词: depth estimation;    gpu;    hyperspectral;    graph cuts;    multiview;    medicine;   

DOI  :  10.3390/s21124091

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown