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

Himar Fabelo, Martin Halicek, Samuel Ortega, Maysam Shahedi, Adam Szolna, Juan Piñeiro, Coralia Sosa, Aruma O'Shanahan, Sara Bisshopp, Carlos Espino, Mariano Márquez, María Hernández (+5 others)
2019 Sensors  
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 founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  ... 
doi:10.3390/s19040920 fatcat:caw2lpoyyveylg5s625p5xfyhe

Trends in Deep Learning for Medical Hyperspectral Image Analysis

Uzair Khan, Sidike Paheding, Colin Elkin, Vijay Devabhaktuni
2021 IEEE Access  
the superficial tumor margin variance with depth [61] Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients  ...  [56] Blood stain classification with hyperspectral imaging and deep neural networks [54] Hyperspectral imaging for glioblastoma surgery: improving tumor identification using a deep spectral-spatial  ... 
doi:10.1109/access.2021.3068392 fatcat:mxse6n6f7bbbrognlnbzponr7u

Front Matter: Volume 10951

Baowei Fei, Cristian A. Linte
2019 Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling  
for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients 10951 11 Development and evaluation of an immersive virtual reality system for medical imaging  ...  A comparison of geometry-and feature-based sparse data extraction for model-based image updating in deep brain stimulation surgery SESSION 2 MOTION COMPENSATION AND TRACKING TECHNIQUES 07 Feasibility  ... 
doi:10.1117/12.2531522 fatcat:6ed6gbuiarfetfqndgjmno772a

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications [chapter]

Samuel Ortega, Martin Halicek, Himar Fabelo, Eduardo Quevedo, Baowei Fei, Gustavo Marrero Callico
2020 Multimedia Information Retrieval [Working Title]  
This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications.  ...  One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis.  ...  This chapter is distributed under the terms of the Creative Commons Attribution License ( by/3.0), which permits unrestricted use, distribution, and reproduction in  ... 
doi:10.5772/intechopen.93960 fatcat:beqkyox6mzhwpg62ncjj2je3ki

Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach

Francesca Manni, Fons van der Sommen, Himar Fabelo, Svitlana Zinger, Caifeng Shan, Erik Edström, Adrian Elmi-Terander, Samuel Ortega, Gustavo Marrero Callicó, Peter H. N. de With
2020 Sensors  
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.  ...  These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.  ...  In this work, a hybrid deep learning-based framework is presented to quantitatively classify brain and tumor tissue on using an in vivo HS brain dataset.  ... 
doi:10.3390/s20236955 pmid:33291409 fatcat:qiko5t64u5gxtpshtt6in2tpt4

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

Himar Fabelo, Samuel Ortega, Adam Szolna, Diederik Bulters, Juan F. Pineiro, Silvester Kabwama, Aruma J-O'Shanahan, Harry Bulstrode, Sara Bisshopp, B. Ravi Kiran, Daniele Ravi, Raquel Lazcano (+16 others)
2019 IEEE Access  
In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented.  ...  The use of hyperspectral imaging for medical applications is becoming more common in recent years.  ...  Fei in the use of medical hyperspectral imaging analysis using deep learning.  ... 
doi:10.1109/access.2019.2904788 fatcat:d2jcmy6qvveozbdyvyvcvhqx4q

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Martin Halicek, Himar Fabelo, Samuel Ortega, Gustavo M. Callico, Baowei Fei
2019 Cancers  
The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo.  ...  In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/cancers11060756 pmid:31151223 pmcid:PMC6627361 fatcat:wiihkhuvrvf2zjl5c3d4h42uxm

Taxonomy Of Brain Tumor Classification Techniques: A Systematic Review

Virupakshappa, Dr. Basavaraj Amarapur
2017 Zenodo  
The main purpose of image processing is to improve the quality of the images for human/machine perception.  ...  The use of digital image processing has become very demanding in various areas including medical applications.  ...  Classification maps from the different machine learning algorithms are evaluated, as well as implementation results on a many core platform, when in-vivo human brain hyperspectral images are employed as  ... 
doi:10.5281/zenodo.1013807 fatcat:srcgtw7mmzdzzhwrnn3w4saigu

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

Himar Fabelo, Samuel Ortega, Daniele Ravi, B. Ravi Kiran, Coralia Sosa, Diederik Bulters, Gustavo M. Callicó, Harry Bulstrode, Adam Szolna, Juan F. Piñeiro, Silvester Kabwama, Daniel Madroñal (+11 others)
2018 PLoS ONE  
However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis.  ...  Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult.  ...  We also thank Paul Grundy and Victoria Wykes, neurosurgeons of the University Hospital of Southampton who helped in this research. Validation  ... 
doi:10.1371/journal.pone.0193721 pmid:29554126 pmcid:PMC5858847 fatcat:gbvr7vjnzbgn5nqhi2ebm3u4ny

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Beatriz Martinez, Raquel Leon, Himar Fabelo, Samuel Ortega, Juan F. Piñeiro, Adam Szolna, Maria Hernandez, Carlos Espino, Aruma J. O'Shanahan, David Carrera, Sara Bisshopp, Coralia Sosa (+5 others)
2019 Sensors  
The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images  ...  The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to  ...  to process in vivo human brain HS data.  ... 
doi:10.3390/s19245481 pmid:31842410 pmcid:PMC6961052 fatcat:ujxpripembd6nln6mlxhmejbdu

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study.  ...  In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Advances in stimulated Raman scattering imaging for tissues and animals

Lingyan Shi, Anthony A Fung, Andy Zhou
2021 Quantitative Imaging in Medicine and Surgery  
Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.  ...  Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more.  ...  (86) also combined SRH with CNN-based deep learning for intraoperative brain tumor diagnosis, and achieved rapid prediction in near real-time.  ... 
doi:10.21037/qims-20-712 pmid:33654679 pmcid:PMC7829158 fatcat:kialw4fpxjhqhievlrhotrxzea

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging

Daniele Ravi, Himar Fabelo, Gustavo Marrero Callic, Guang-Zhong Yang
2017 IEEE Transactions on Medical Imaging  
Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging.  ...  Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive.  ...  Adam Szolna from the Hospital Doctor Negrín for their valuable contributions.  ... 
doi:10.1109/tmi.2017.2695523 pmid:28436854 fatcat:2rgiiegzujekbklniz3emdmzqu

Repurposing Molecular Imaging and Sensing for Cancer Image-Guided Surgery

Suman Mondal, Christine O'Brien, Kevin Bishop, Ryan Fields, Julie Margenthaler, Samuel Achilefu
2020 Journal of Nuclear Medicine  
Cancer imaging, in particular, has leveraged advances in molecular imaging agents and technology to improve the accuracy of tumor detection, interrogate tumor heterogeneity, monitor treatment response,  ...  Emerging low cost, portable, and user-friendly imaging systems make the case for adopting some of these technologies as the global standard of care in surgical practice.  ...  For example, emphasis on specific tissue Raman signatures has enabled identification of ex vivo oral cancers (76, 77) , assessment of breast cancer margins from in vivo (9) and ex vivo specimens (78,  ... 
doi:10.2967/jnumed.118.220426 pmid:32303598 pmcid:PMC7413229 fatcat:zh2xg6cg7vhojebbed54vrvbka

Optical aspects of a miniature fluorescence microscope for super-sensitive biomedical detection

Yunfeng Nie, Aikio Sanna, Annukka Kokkonen, Teemu Sipola, Uusitalo Sanna, Simonetta Grilli, Heidi Ottevaere
2020 Zenodo  
We present optical design and the principle demonstrator of a miniature fluorescence microscope aiming for super-sensitive detection.  ...  Current commercial fluorescence microscopes are typically sophisticated, bulky and expensive, not suitable for low-volume or clinic routine biomedical detection.  ...  Rowlands 1,2 ; 1 Dept. of Bioengineering, Imperial College London, UK; 2 Centre for Neurotechnology, Imperial College London, UK; 3 Dept. of Brain Sciences, Imperial College London, UK.  ... 
doi:10.5281/zenodo.3822435 fatcat:3eoome22a5grbmarbrktphfh7a
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