A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples

2016 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Raùl Guerra ◽  
Gustavo Callicó ◽  
Adam Szolna ◽  
...  
Keyword(s):  
Human Brain ◽  
Cancer Detection ◽  
Brain Cancer ◽  
Hyperspectral Images

scholarly journals In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39098-39116 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Adam Szolna ◽  
Diederik Bulters ◽  
Juan F. Pineiro ◽  
...  
Keyword(s):  
Human Brain ◽  
Cancer Detection ◽  
Brain Cancer ◽  
Image Database ◽  
Brain Image

scholarly journals Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193721 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Daniele Ravi ◽  
B. Ravi Kiran ◽  
Coralia Sosa ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Brain Cancer ◽  
Hyperspectral Images ◽  
Spectral Classification ◽  
Surgical Operations

scholarly journals Detection of human brain cancer infiltration ex vivo and in vivo using quantitative optical coherence tomography

2015 ◽  
Vol 7 (292) ◽  
pp. 292ra100-292ra100 ◽  
Author(s):  
Carmen Kut ◽  
Kaisorn L. Chaichana ◽  
Jiefeng Xi ◽  
Shaan M. Raza ◽  
Xiaobu Ye ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Human Brain ◽  
Brain Cancer ◽  
Ex Vivo ◽  
Optical Coherence

scholarly journals Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 920 ◽  
Author(s):  
Himar Fabelo ◽  
Martin Halicek ◽  
Samuel Ortega ◽  
Maysam Shahedi ◽  
Adam Szolna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Tissue ◽  
Tumor Resection ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Label Free ◽  
Thematic Map ◽  
Operating Surgeon

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

scholarly journals Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 283 ◽  
Author(s):  
Emanuele Torti ◽  
Giordana Florimbi ◽  
Francesca Castelli ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
...  
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Cancer Detection ◽  
Surgical Procedures ◽  
Brain Cancer ◽  
Clustering Algorithm ◽  
Tumor Resection ◽  
Unsupervised Segmentation ◽  
Real Time Processing

The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.

scholarly journals P03.18 Detection of human brain cancer in pathological slides using hyperspectral images

2017 ◽  
Vol 19 (suppl_3) ◽  
pp. iii37-iii37 ◽  
Author(s):  
S. Ortega ◽  
H. Fabelo ◽  
R. Camacho ◽  
M. L. Plaza ◽  
G. M. Callico ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Cancer ◽  
Hyperspectral Images

scholarly journals Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1503 ◽  
Author(s):  
Emanuele Torti ◽  
Raquel Leon ◽  
Marco La Salvia ◽  
Giordana Florimbi ◽  
Beatriz Martinez-Vega ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Hyperspectral Imaging ◽  
Cancer Detection ◽  
Medical Practice ◽  
Skin Lesions ◽  
Hyperspectral Images ◽  
Parallel Pipeline ◽  
Many Core ◽  
Skin Cancer Detection

The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists’ expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s.

In Vivo 1H MR Spectroscopic Imaging of Human Brain

1994 ◽  
Vol 31 (2) ◽  
pp. 185
Author(s):  
Yong Whee Bahk ◽  
Kyung Sub Shinn ◽  
Tae Suk Suh ◽  
Bo Young Choe ◽  
Kyo Ho Choi
Keyword(s):  
Human Brain ◽  
Spectroscopic Imaging ◽  
Mr Spectroscopic Imaging

In vivo magnetic resonance imaging and 31P spectroscopy of large human brain tumours at 1.5 tesla

1988 ◽  
Vol 29 (1) ◽  
pp. 77-82 ◽  
Author(s):  
C. Thomsen ◽  
K. E. Jensen ◽  
E. Achten ◽  
O. Henriksen
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Brain Tumours ◽  
Resonance Imaging ◽  
Human Brain Tumours
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