Abstract
The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D–2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D–2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.
Original language | English |
---|---|
Article number | 6955 |
Number of pages | 20 |
Journal | Sensors |
Volume | 20 |
Issue number | 23 |
DOIs | |
Publication status | Published - 5 Dec 2020 |
Funding
Funding: The research activity leading to the results of this paper was funded by the H2020-ECSEL Joint Undertaking under Grant Agreement No. 692470 (ASTONISH Project). Additionally, this research was supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperspectral Identification of Brain Tumors” under Grant Agreement ProID2017010164.
Keywords
- Ant-colony-based band selection
- Brain imaging
- Deep learning
- Glioblastoma
- Hyperspectral imaging
- Tumor tissue classification
- Glioblastoma/diagnostic imaging
- Neural Networks, Computer
- Brain Neoplasms/diagnostic imaging
- Artificial Intelligence
- Humans
- Hyperspectral Imaging