Towards Real-Time Computing of Intraoperative Hyperspectral Imaging for Brain Cancer Detection Using Multi-GPU Platforms

Abstract

Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons' naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient's quality of life. The brain cancer detection system developed as part of the ''HypErspectraL Imaging Cancer Detection'' (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future.

Comparison of different spectral cameras for image-guided organ transplantation

“…In combination with previously published literature about optical organ and tissue behaviors, 37 , 39 , 43 – 47 this study allows the selection of the optimal HMSI system for the use for a specific medical applications. 12 , 31 , 48 , 49 …”

Hyperspectral Imaging for Clinical Applications

“…In brain surgery, minimally invasive surgery is critical to preserve brain function. HSI was employed to detect brain tumor margin under in-vivo imaging conditions, which helps surgeons determine optimal excision areas [53,83,84]. Functional imaging via HSI has been applied to assess physiological properties, such as blood and oxygen supply in anastomotic areas after oesophagectomy [85], liver resection [86], and colorectal resection [87].…”

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

“…Among the various available parallel technologies, the Graphics Processing Unit (GPU) is the most attractive solution, which can execute complex, intrinsically parallel algorithms on large amounts of data. Florimbi, et al employed GPU technology to classify the largest (worst-case) image in the database in less than 3 s, which satisfied the surgery limitation of real-time setting within 1 min, and became a potential method for hyperspectral video processing in the immediate future ( Florimbi et al, 2020 ).…”