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Ma L, Pruitt K, Fei B. A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12930:129300Z. [PMID: 38745746 PMCID: PMC11093589 DOI: 10.1117/12.3008789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.
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Affiliation(s)
- Ling Ma
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
| | - Kelden Pruitt
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
| | - Baowei Fei
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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Ma L, Pruitt K, Fei B. Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12928:129280I. [PMID: 38752166 PMCID: PMC11094590 DOI: 10.1117/12.3006573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Laparoscopic and robotic surgery, as one type of minimally invasive surgery (MIS), has gained popularity due to the improved surgeon ergonomics, instrument precision, operative time, and postoperative recovery. Hyperspectral imaging (HSI) is an emerging medical imaging modality, which has proved useful for intraoperative image guidance. Snapshot hyperspectral cameras are ideal for intraoperative laparoscopic imaging because of their compact size and light weight, but low spatial resolution can be a limitation. In this work, we developed a dual-camera laparoscopic imaging system that consists of a high-resolution color camera and a snapshot hyperspectral camera, and we employed super-resolution reconstruction to fuse the images from both cameras to generate high-resolution hyperspectral images. The experimental results show that our method can significantly improve the resolution of hyperspectral images without compromising the image quality or spectral signatures. The proposed super-resolution reconstruction method is promising to promote the employment of high-speed hyperspectral imaging in laparoscopic surgery.
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Affiliation(s)
- Ling Ma
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
| | - Kelden Pruitt
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
| | - Baowei Fei
- Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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Liao WC, Mukundan A, Sadiaza C, Tsao YM, Huang CW, Wang HC. Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4383-4405. [PMID: 37799695 PMCID: PMC10549751 DOI: 10.1364/boe.492635] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 10/07/2023]
Abstract
One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.
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Affiliation(s)
- Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Cleorita Sadiaza
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila, 1015, Philippines
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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Witteveen M, Sterenborg HJCM, van Leeuwen TG, Aalders MCG, Ruers TJM, Post AL. Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:106003. [PMID: 36207772 PMCID: PMC9541333 DOI: 10.1117/1.jbo.27.10.106003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples. AIM To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types. APPROACH We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast. CONCLUSIONS Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.
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Affiliation(s)
- Mark Witteveen
- the Netherlands Cancer Institute, Surgical Oncology, Amsterdam, The Netherlands
- University of Twente, Science and Technology, Nanobiophysics, Enschede, The Netherlands
| | - Henricus J. C. M. Sterenborg
- the Netherlands Cancer Institute, Surgical Oncology, Amsterdam, The Netherlands
- Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Ton G. van Leeuwen
- Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Maurice C. G. Aalders
- Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
- University of Amsterdam, Co van Ledden Hulsebosch Center, Amsterdam, The Netherlands
| | - Theo J. M. Ruers
- the Netherlands Cancer Institute, Surgical Oncology, Amsterdam, The Netherlands
- University of Twente, Science and Technology, Nanobiophysics, Enschede, The Netherlands
| | - Anouk L. Post
- the Netherlands Cancer Institute, Surgical Oncology, Amsterdam, The Netherlands
- Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
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Tong Y, Ach T, Curcio CA, Smith RT. Hyperspectral autofluorescence characterization of drusen and sub-RPE deposits in age-related macular degeneration. ACTA ACUST UNITED AC 2021; 6. [PMID: 33791592 PMCID: PMC8009528 DOI: 10.21037/aes-20-12] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Background: Soft drusen and basal linear deposit (BLinD) are two forms of the same extracellular lipid rich material that together make up an Oil Spill on Bruch’s membrane (BrM). Drusen are focal and can be recognized clinically. In contrast BLinD is thin and diffusely distributed, and invisible clinically, even on highest resolution OCT, but has been detected on en face hyperspectral autofluorescence (AF) imaging ex vivo. We sought to optimize histologic hyperspectral AF imaging and image analysis for recognition of drusen and sub-RPE deposits (including BLinD and basal laminar deposit), for potential clinical application. Methods: Twenty locations specifically with drusen and 12 additional locations specifically from fovea, perifovea and mid-periphery from RPE/BrM flatmounts from 4 AMD donors underwent hyperspectral AF imaging with 4 excitation wavelengths (λex 436, 450, 480 and 505 nm), and the resulting image cubes were simultaneously decomposed with our published non-negative matrix factorization (NMF). Rank 4 recovery of 4 emission spectra was chosen for each excitation wavelength. Results: A composite emission spectrum, sensitive and specific for drusen and presumed sub-RPE deposits (the SDr spectrum) was recovered with peak at 510–520 nm in all tissues with drusen, with greatest amplitudes at excitations λex 436, 450 and 480 nm. The RPE spectra of combined sources Lipofuscin (LF)/Melanolipofuscin (MLF) were of comparable amplitude and consistently recapitulated the spectra S1, S2 and S3 previously reported from all tissues: tissues with drusen, foveal and extra-foveal locations. Conclusions: A clinical hyperspectral AF camera, with properly chosen excitation wavelengths in the blue range and a hyperspectral AF detector, should be capable of detecting and quantifying drusen and sub-RPE deposits, the earliest known lesions of AMD, before any other currently available imaging modality.
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Affiliation(s)
- Yuehong Tong
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Ach
- Department of Ophthalmology, University Hospital Bonn, Germany
| | - Christine A Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, AL, USA
| | - R Theodore Smith
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Ma L, Fei B. Comprehensive review of surgical microscopes: technology development and medical applications. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200292VRR. [PMID: 33398948 PMCID: PMC7780882 DOI: 10.1117/1.jbo.26.1.010901] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/04/2020] [Indexed: 05/06/2023]
Abstract
SIGNIFICANCE Surgical microscopes provide adjustable magnification, bright illumination, and clear visualization of the surgical field and have been increasingly used in operating rooms. State-of-the-art surgical microscopes are integrated with various imaging modalities, such as optical coherence tomography (OCT), fluorescence imaging, and augmented reality (AR) for image-guided surgery. AIM This comprehensive review is based on the literature of over 500 papers that cover the technology development and applications of surgical microscopy over the past century. The aim of this review is threefold: (i) providing a comprehensive technical overview of surgical microscopes, (ii) providing critical references for microscope selection and system development, and (iii) providing an overview of various medical applications. APPROACH More than 500 references were collected and reviewed. A timeline of important milestones during the evolution of surgical microscope is provided in this study. An in-depth technical overview of the optical system, mechanical system, illumination, visualization, and integration with advanced imaging modalities is provided. Various medical applications of surgical microscopes in neurosurgery and spine surgery, ophthalmic surgery, ear-nose-throat (ENT) surgery, endodontics, and plastic and reconstructive surgery are described. RESULTS Surgical microscopy has been significantly advanced in the technical aspects of high-end optics, bright and shadow-free illumination, stable and flexible mechanical design, and versatile visualization. New imaging modalities, such as hyperspectral imaging, OCT, fluorescence imaging, photoacoustic microscopy, and laser speckle contrast imaging, are being integrated with surgical microscopes. Advanced visualization and AR are being added to surgical microscopes as new features that are changing clinical practices in the operating room. CONCLUSIONS The combination of new imaging technologies and surgical microscopy will enable surgeons to perform challenging procedures and improve surgical outcomes. With advanced visualization and improved ergonomics, the surgical microscope has become a powerful tool in neurosurgery, spinal, ENT, ophthalmic, plastic and reconstructive surgeries.
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Affiliation(s)
- Ling Ma
- University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States
| | - Baowei Fei
- University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States
- University of Texas Southwestern Medical Center, Department of Radiology, Dallas, Texas, United States
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Saiko G, Lombardi P, Au Y, Queen D, Armstrong D, Harding K. Hyperspectral imaging in wound care: A systematic review. Int Wound J 2020; 17:1840-1856. [PMID: 32830443 PMCID: PMC7949456 DOI: 10.1111/iwj.13474] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/07/2020] [Accepted: 07/15/2020] [Indexed: 01/18/2023] Open
Abstract
Multispectral and hyperspectral imaging (HSI) are emerging imaging techniques with the potential to transform the way patients with wounds are cared for, but it is not clear whether current systems are capable of delivering real-time tissue characterisation and treatment guidance. We conducted a systematic review of HSI systems that have been assessed in patients, published over the past 32 years. We analysed 140 studies, including 10 different HSI systems. Current in vivo HSI systems generate a tissue oxygenation map. Tissue oxygenation measurements may help to predict those patients at risk of wound formation or delayed healing. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of in vivo label-free HSI, but further work is needed to fully integrate it into the current clinical workflow for different wound aetiologies. As an emerging imaging modality for medical applications, HSI offers great potential for non-invasive disease diagnosis and guidance when treating patients with both acute and chronic wounds.
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Affiliation(s)
| | | | | | | | - David Armstrong
- Keck School of MedicineUniversity of Southern California, Los AngelesCaliforniaCaliforniaCanada
| | - Keith Harding
- School of MedicineCardiff UniversityWalesUK
- A*STARSingapore
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Saiko G, Lombardi P, Au Y, Queen D, Armstrong D, Harding K. Hyperspectral imaging in wound care: A systematic review. Int Wound J 2020. [PMID: 32830443 DOI: 10.1111/iwj.13474.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Multispectral and hyperspectral imaging (HSI) are emerging imaging techniques with the potential to transform the way patients with wounds are cared for, but it is not clear whether current systems are capable of delivering real-time tissue characterisation and treatment guidance. We conducted a systematic review of HSI systems that have been assessed in patients, published over the past 32 years. We analysed 140 studies, including 10 different HSI systems. Current in vivo HSI systems generate a tissue oxygenation map. Tissue oxygenation measurements may help to predict those patients at risk of wound formation or delayed healing. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of in vivo label-free HSI, but further work is needed to fully integrate it into the current clinical workflow for different wound aetiologies. As an emerging imaging modality for medical applications, HSI offers great potential for non-invasive disease diagnosis and guidance when treating patients with both acute and chronic wounds.
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Affiliation(s)
| | | | | | | | - David Armstrong
- Keck School of Medicine, University of Southern California, Los Angeles, California, California, Canada
| | - Keith Harding
- School of Medicine, Cardiff University, Wales, UK.,A*STAR, Singapore
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Martinez B, Leon R, Fabelo H, Ortega S, Piñeiro JF, Szolna A, Hernandez M, Espino C, J. O’Shanahan A, Carrera D, Bisshopp S, Sosa C, Marquez M, Camacho R, Plaza MDLL, Morera J, M. Callico G. Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging. SENSORS (BASEL, SWITZERLAND) 2019; 19:E5481. [PMID: 31842410 PMCID: PMC6961052 DOI: 10.3390/s19245481] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/01/2019] [Accepted: 12/10/2019] [Indexed: 02/07/2023]
Abstract
Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. 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. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. 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 the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.
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Affiliation(s)
- Beatriz Martinez
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain; (R.L.); (H.F.); (S.O.); (G.M.C.)
| | - Raquel Leon
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain; (R.L.); (H.F.); (S.O.); (G.M.C.)
| | - Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain; (R.L.); (H.F.); (S.O.); (G.M.C.)
| | - Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain; (R.L.); (H.F.); (S.O.); (G.M.C.)
| | - Juan F. Piñeiro
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Adam Szolna
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Maria Hernandez
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Carlos Espino
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Aruma J. O’Shanahan
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - David Carrera
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Sara Bisshopp
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Coralia Sosa
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Mariano Marquez
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Rafael Camacho
- Department of Pathological Anatomy, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (R.C.); (M.d.l.L.P.)
| | - Maria de la Luz Plaza
- Department of Pathological Anatomy, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (R.C.); (M.d.l.L.P.)
| | - Jesus Morera
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, 35010 Barranco de la Ballena s/n, Las Palmas de Gran Canaria, Spain; (J.F.P.); (A.S.); (M.H.); (C.E.); (A.J.O.); (D.C.); (S.B.); (C.S.); (M.M.); (J.M.)
| | - Gustavo M. Callico
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain; (R.L.); (H.F.); (S.O.); (G.M.C.)
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Halicek M, Fabelo H, Ortega S, Callico GM, Fei B. In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer. Cancers (Basel) 2019; 11:E756. [PMID: 31151223 PMCID: PMC6627361 DOI: 10.3390/cancers11060756] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 05/20/2019] [Accepted: 05/24/2019] [Indexed: 12/27/2022] Open
Abstract
In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. 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 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. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.
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Affiliation(s)
- Martin Halicek
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA.
- Department of Biomedical Engineering, Emory University and The Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, GA 30329, USA.
| | - Himar Fabelo
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA.
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Gustavo M Callico
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA.
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd, Dallas, TX 75390, USA.
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd, Dallas, TX 75390, USA.
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Ortega S, Fabelo H, Iakovidis DK, Koulaouzidis A, Callico GM. Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some⁻Different⁻Light into the Dark. J Clin Med 2019; 8:E36. [PMID: 30609685 PMCID: PMC6352071 DOI: 10.3390/jcm8010036] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 12/14/2018] [Accepted: 12/26/2018] [Indexed: 01/27/2023] Open
Abstract
Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the potential use of these technologies in the medical domain, with emphasis in the current advances in gastroenterology. The main aim of this review is to provide an overview of contemporary concepts regarding HSI technology together with state-of-art systems and applications in gastroenterology. Finally, we discuss the current limitations and upcoming trends of HSI in gastroenterology.
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Affiliation(s)
- Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria 35017, Spain.
| | - Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria 35017, Spain.
| | - Dimitris K Iakovidis
- Dept. of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece.
| | | | - Gustavo M Callico
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria 35017, Spain.
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12
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Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, Callicó GM, Bulstrode H, Szolna A, Piñeiro JF, Kabwama S, Madroñal D, Lazcano R, J-O’Shanahan A, Bisshopp S, Hernández M, Báez A, Yang GZ, Stanciulescu B, Salvador R, Juárez E, Sarmiento R. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS One 2018; 13:e0193721. [PMID: 29554126 PMCID: PMC5858847 DOI: 10.1371/journal.pone.0193721] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 02/06/2018] [Indexed: 11/18/2022] Open
Abstract
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. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.
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Affiliation(s)
- Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
- * E-mail:
| | - Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Daniele Ravi
- The Hamlyn Centre, Imperial College London (ICL), London, United Kingdom
| | - B. Ravi Kiran
- Laboratoire CRISTAL, Université Lille 3, Villeneuve-d’Ascq, France
| | - Coralia Sosa
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Diederik Bulters
- Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton, United Kingdom
| | - Gustavo M. Callicó
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Harry Bulstrode
- Department of Neurosurgery, Addenbrookes Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Adam Szolna
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Juan F. Piñeiro
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Silvester Kabwama
- Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton, United Kingdom
| | - Daniel Madroñal
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Raquel Lazcano
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Aruma J-O’Shanahan
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Sara Bisshopp
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - María Hernández
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Abelardo Báez
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Guang-Zhong Yang
- The Hamlyn Centre, Imperial College London (ICL), London, United Kingdom
| | - Bogdan Stanciulescu
- Ecole Nationale Supérieure des Mines de Paris (ENSMP), MINES ParisTech, Paris, France
| | - Rubén Salvador
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Eduardo Juárez
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Roberto Sarmiento
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
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Halicek M, Little JV, Wang X, Patel M, Griffith CC, Chen AY, Fei B. Tumor Margin Classification of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10576:1057605. [PMID: 30245540 PMCID: PMC6149520 DOI: 10.1117/12.2293167] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.
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Affiliation(s)
- Martin Halicek
- Georgia Institute of Technology & Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, USA
- Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - James V Little
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, GA, USA
| | - Xu Wang
- Emory University School of Medicine, Department of Hematology and Medical Oncology, Atlanta, GA, USA
| | - Mihir Patel
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, GA, USA
- Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - Christopher C Griffith
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, GA, USA
| | - Amy Y Chen
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, GA, USA
- Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - Baowei Fei
- Georgia Institute of Technology & Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, USA
- Winship Cancer Institute of Emory University, Atlanta, GA, USA
- Emory University, Department of Mathematics and Computer Science, Atlanta, GA, USA
- Emory University, Department of Radiology and Imaging Sciences, Atlanta, GA, USA
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Halicek M, Lu G, Little JV, Wang X, Patel M, Griffith CC, El-Deiry MW, Chen AY, Fei B. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:60503. [PMID: 28655055 PMCID: PMC5482930 DOI: 10.1117/1.jbo.22.6.060503] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 06/09/2017] [Indexed: 05/03/2023]
Abstract
Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.
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Affiliation(s)
- Martin Halicek
- Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Medical College of Georgia, Augusta, Georgia, United States
| | - Guolan Lu
- Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - James V. Little
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Xu Wang
- Emory University School of Medicine, Department of Hematology and Medical Oncology, Atlanta, Georgia, United States
| | - Mihir Patel
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, Georgia, United States
- Winship Cancer Institute of Emory University, Atlanta, Georgia, United States
| | - Christopher C. Griffith
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Mark W. El-Deiry
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, Georgia, United States
- Winship Cancer Institute of Emory University, Atlanta, Georgia, United States
| | - Amy Y. Chen
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, Georgia, United States
- Winship Cancer Institute of Emory University, Atlanta, Georgia, United States
| | - Baowei Fei
- Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Winship Cancer Institute of Emory University, Atlanta, Georgia, United States
- Emory University, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United States
- Emory University, Department of Mathematics and Computer Science, Atlanta, Georgia, United States
- Address all correspondence to: Baowei Fei, E-mail:
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15
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Lu G, Qin X, Wang D, Muller S, Zhang H, Chen A, Chen ZG, Fei B. Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788. [PMID: 27656034 DOI: 10.1117/12.2216553] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.
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Affiliation(s)
- Guolan Lu
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Xulei Qin
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Dongsheng Wang
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA
| | - Susan Muller
- Department of Otolaryngology, Emory University School of Medicine, Atlanta, GA
| | - Hongzheng Zhang
- Department of Otolaryngology, Emory University School of Medicine, Atlanta, GA
| | - Amy Chen
- Department of Otolaryngology, Emory University School of Medicine, Atlanta, GA
| | - Zhuo Georgia Chen
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA
| | - Baowei Fei
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Mathematics & Computer Science, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA
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16
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Pike R, Lu G, Wang D, Chen ZG, Fei B. A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging. IEEE Trans Biomed Eng 2016; 63:653-63. [PMID: 26285052 PMCID: PMC4791052 DOI: 10.1109/tbme.2015.2468578] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
GOAL The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. METHODS An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. CONCLUSION The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. SIGNIFICANCE Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.
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Chung H, Lu G, Tian Z, Wang D, Chen ZG, Fei B. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788:978813. [PMID: 27656035 PMCID: PMC5028206 DOI: 10.1117/12.2216559] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.
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Affiliation(s)
- Hyunkoo Chung
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Guolan Lu
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Dongsheng Wang
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA
| | - Zhuo Georgia Chen
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA
| | - Baowei Fei
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Mathematics Computer Science, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA
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18
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Lu G, Wang D, Qin X, Halig L, Muller S, Zhang H, Chen A, Pogue BW, Chen ZG, Fei B. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. JOURNAL OF BIOMEDICAL OPTICS 2015; 20:126012. [PMID: 26720879 PMCID: PMC4691647 DOI: 10.1117/1.jbo.20.12.126012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2015] [Accepted: 11/25/2015] [Indexed: 05/15/2023]
Abstract
Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.
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Affiliation(s)
- Guolan Lu
- Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United States
| | - Dongsheng Wang
- Emory University, School of Medicine, Department of Hematology and Medical Oncology, , Atlanta, Georgia 30332, United States
| | - Xulei Qin
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States
| | - Luma Halig
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States
| | - Susan Muller
- Emory University, School of Medicine, Department of Otolaryngology, , Atlanta, Georgia 30332, United States
| | - Hongzheng Zhang
- Emory University, School of Medicine, Department of Otolaryngology, , Atlanta, Georgia 30332, United States
| | - Amy Chen
- Emory University, School of Medicine, Department of Otolaryngology, , Atlanta, Georgia 30332, United States
| | - Brian W. Pogue
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire 03755, United States
| | - Zhuo Georgia Chen
- Emory University, School of Medicine, Department of Hematology and Medical Oncology, , Atlanta, Georgia 30332, United States
| | - Baowei Fei
- Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United States
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States
- Winship Cancer Institute of Emory University, Atlanta, Georgia 30322, United States
- Address all correspondence to: Baowei Fei, E-mail:
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