1
|
Zhang C, Zhang Z, Yu D, Cheng Q, Shan S, Li M, Mou L, Yang X, Ma X. Unsupervised band selection of medical hyperspectral images guided by data gravitation and weak correlation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107721. [PMID: 37506601 DOI: 10.1016/j.cmpb.2023.107721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/06/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND AND OBJECTIVE Medical hyperspectral images (MHSIs) are used for a contact-free examination of patients without harmful radiation. However, high-dimensionality images contain large amounts of data that are sparsely distributed in a high-dimensional space, which leads to the "curse of dimensionality" (called Hughes' phenomenon) and increases the complexity and cost of data processing and storage. Hence, there is a need for spectral dimensionality reduction before the clinical application of MHSIs. Some dimensionality-reducing strategies have been proposed; however, they distort the data within MHSIs. METHODS To compress dimensionality without destroying the original data structure, we propose a method that involves data gravitation and weak correlation-based ranking (DGWCR) for removing bands of noise from MHSIs while clustering signal-containing bands. Band clustering is done by using the connection centre evolution (CCE) algorithm and selecting the most representative bands in each cluster based on the composite force. The bands within the clusters are ranked using the new entropy-containing matrix, and a global ranking of bands is obtained by applying an S-shaped strategy. The source code is available at https://www.github.com/zhangchenglong1116/DGWCR. RESULTS Upon feeding the reduced-dimensional images into various classifiers, the experimental results demonstrated that the small number of bands selected by the proposed DGWCR consistently achieved higher classification accuracy than the original data. Unlike other reference methods (e.g. the latest deep-learning-based strategies), DGWCR chooses the spectral bands with the least redundancy and greatest discrimination. CONCLUSION In this study, we present a method for efficient band selection for MHSIs that alleviates the "curse of dimensionality". Experiments were validated with three MHSIs in the human brain, and they outperformed several other band selection methods, demonstrating the clinical potential of DGWCR.
Collapse
Affiliation(s)
- Chenglong Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Zhimin Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Dexin Yu
- Radiology Department, Qilu Hospital of Shandong University, Jinan 250000, China
| | - Qiyuan Cheng
- Medical Engineering Department, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, China
| | - Shihao Shan
- School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Mengjiao Li
- School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Lichao Mou
- Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, 80333, Germany
| | - Xiaoli Yang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China; Weifang Xinli Superconducting Magnet Technology Co., Ltd, Weifang 261005, China.
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan 250061, China.
| |
Collapse
|
2
|
Ayala L, Isensee F, Wirkert SJ, Vemuri AS, Maier-Hein KH, Fei B, Maier-Hein L. Band selection for oxygenation estimation with multispectral/hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2022; 13:1224-1242. [PMID: 35414995 PMCID: PMC8973188 DOI: 10.1364/boe.441214] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 05/24/2023]
Abstract
Multispectral imaging provides valuable information on tissue composition such as hemoglobin oxygen saturation. However, the real-time application of this technique in interventional medicine can be challenging due to the long acquisition times needed for large amounts of hyperspectral data with hundreds of bands. While this challenge can partially be addressed by choosing a discriminative subset of bands, the band selection methods proposed to date are mainly restricted by the availability of often hard to obtain reference measurements. We address this bottleneck with a new approach to band selection that leverages highly accurate Monte Carlo (MC) simulations. We hypothesize that a so chosen small subset of bands can reproduce or even improve upon the results of a quasi continuous spectral measurement. We further investigate whether novel domain adaptation techniques can address the inevitable domain shift stemming from the use of simulations. Initial results based on in silico and in vivo experiments suggest that 10-20 bands are sufficient to closely reproduce results from spectral measurements with 101 bands in the 500-700 nm range. The investigated domain adaptation technique, which only requires unlabeled in vivo measurements, yielded better results than the pure in silico band selection method. Overall, our method could guide development of fast multispectral imaging systems suited for interventional use without relying on complex hardware setups or manually labeled data.
Collapse
Affiliation(s)
- Leonardo Ayala
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
- Authors contributed equally
| | - Fabian Isensee
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Applied Computer Vision Lab, Helmholtz Imaging, Dallas, Texas 75001, USA
- Authors contributed equally
| | - Sebastian J Wirkert
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anant S Vemuri
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Maier-Hein
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas 75080-4551, USA
- Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, Texas 75001, USA
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, Texas 75001, USA
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| |
Collapse
|
3
|
Waterhouse DJ, Bano S, Januszewicz W, Stoyanov D, Fitzgerald RC, di Pietro M, Bohndiek SE. First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210159R. [PMID: 34628734 PMCID: PMC8501416 DOI: 10.1117/1.jbo.26.10.106002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 05/04/2023]
Abstract
SIGNIFICANCE The early detection of dysplasia in patients with Barrett's esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy. AIM We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract. APPROACH We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, k-nearest neighbor classification, and a neural network. RESULTS Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and k-nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett's esophagus and neoplasia based on majority decisions per-image. CONCLUSIONS MSI shows promise for disease classification in Barrett's esophagus and merits further investigation as a tool in high-definition "chip-on-tip" endoscopes.
Collapse
Affiliation(s)
- Dale J. Waterhouse
- University of Cambridge, Department of Physics and CRUK Cambridge Institute, Cambridge, United Kingdom
- University College London, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
| | - Sophia Bano
- University College London, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
| | - Wladyslaw Januszewicz
- Medical Centre for Postgraduate Education, Department of Gastroenterology, Hepatology and Clinical Oncology, Warsaw, Poland
| | - Dan Stoyanov
- University College London, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
| | - Rebecca C. Fitzgerald
- University of Cambridge, MRC Cancer Unit, Hutchison/MRC Research Centre, Cambridge, United Kingdom
| | - Massimiliano di Pietro
- University of Cambridge, MRC Cancer Unit, Hutchison/MRC Research Centre, Cambridge, United Kingdom
| | - Sarah E. Bohndiek
- University of Cambridge, Department of Physics and CRUK Cambridge Institute, Cambridge, United Kingdom
| |
Collapse
|
4
|
Chiba T, Murata M, Kawano T, Hashizume M, Akahoshi T. Reflectance spectra analysis for mucous assessment. World J Gastrointest Oncol 2021; 13:822-834. [PMID: 34457188 PMCID: PMC8371524 DOI: 10.4251/wjgo.v13.i8.822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/26/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
This review report represents an overview of research and development on medical hyperspectral imaging technology and its applications. Spectral imaging technology is attracting attention as a new imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. Considering the recent advances in imaging, this technology provides an opportunity for two-dimensional mapping of oxygen saturation (SatO2) of blood with high accuracy, spatial spectral imaging, and its analysis and provides detection and diagnostic information about the tissue physiology and morphology. Multispectral imaging also provides information about tissue oxygenation, perfusion, and potential function during surgery. Analytical algorithm has been examined, and indication of accurate map of relative hemoglobin concentration and SatO2 can be indicated with preferable resolution and frame rate. This technology is expected to provide promising biomedical information in practical use. Several studies suggested that blood flow and SatO2 are associated with gastrointestinal disorders, particularly malignant tumor conditions. The use and analysis of spectroscopic images are expected to potentially play a role in the detection and diagnosis of these diseases.
Collapse
Affiliation(s)
- Toru Chiba
- Pentax_LifeCare, HOYA Corporation, Akishima-shi 196-0012, Tokyo, Japan
| | - Masaharu Murata
- Center for Advanced Medical Innovation, Kyushu University, Fukuoka-shi 812-8582, Fukuoka, Japan
| | - Takahito Kawano
- Center for Advanced Medical Innovation, Kyushu University, Fukuoka-shi 812-8582, Fukuoka, Japan
| | - Makoto Hashizume
- Center for Advanced Medical Innovation, Kyushu University, Fukuoka-shi 812-8582, Fukuoka, Japan
| | - Tomohiko Akahoshi
- Department of Disaster and Emergency Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka_shi 812-8582, Fukuoka, Japan
| |
Collapse
|
5
|
He Z, Wang P, Ye X. Novel endoscopic optical diagnostic technologies in medical trial research: recent advancements and future prospects. Biomed Eng Online 2021; 20:5. [PMID: 33407477 PMCID: PMC7789310 DOI: 10.1186/s12938-020-00845-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 12/23/2020] [Indexed: 12/20/2022] Open
Abstract
Novel endoscopic biophotonic diagnostic technologies have the potential to non-invasively detect the interior of a hollow organ or cavity of the human body with subcellular resolution or to obtain biochemical information about tissue in real time. With the capability to visualize or analyze the diagnostic target in vivo, these techniques gradually developed as potential candidates to challenge histopathology which remains the gold standard for diagnosis. Consequently, many innovative endoscopic diagnostic techniques have succeeded in detection, characterization, and confirmation: the three critical steps for routine endoscopic diagnosis. In this review, we mainly summarize researches on emerging endoscopic optical diagnostic techniques, with emphasis on recent advances. We also introduce the fundamental principles and the development of those techniques and compare their characteristics. Especially, we shed light on the merit of novel endoscopic imaging technologies in medical research. For example, hyperspectral imaging and Raman spectroscopy provide direct molecular information, while optical coherence tomography and multi-photo endomicroscopy offer a more extensive detection range and excellent spatial-temporal resolution. Furthermore, we summarize the unexplored application fields of these endoscopic optical techniques in major hospital departments for biomedical researchers. Finally, we provide a brief overview of the future perspectives, as well as bottlenecks of those endoscopic optical diagnostic technologies. We believe all these efforts will enrich the diagnostic toolbox for endoscopists, enhance diagnostic efficiency, and reduce the rate of missed diagnosis and misdiagnosis.
Collapse
Affiliation(s)
- Zhongyu He
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Peng Wang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Xuesong Ye
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.
- State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, 310058, People's Republic of China.
| |
Collapse
|
6
|
He X, Liu Y, Beckett P, Uddin MH, Nirmalathas A, Unnithan RR. Hybrid Color Filters for Multispectral Imaging. ADVANCED THEORY AND SIMULATIONS 2020. [DOI: 10.1002/adts.202000137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Xin He
- Department of Electrical and Electronic Engineering The University of Melbourne Melbourne VIC 3010 Australia
| | - Yajing Liu
- Department of Electrical and Electronic Engineering The University of Melbourne Melbourne VIC 3010 Australia
| | - Paul Beckett
- School of Engineering RMIT University Melbourne VIC 3000 Australia
| | - Md Hemayet Uddin
- Melbourne Centre for Nanofabrication Australian National Fabrication Facility Clayton VIC 3168 Australia
| | | | | |
Collapse
|
7
|
Clancy NT, Jones G, Maier-Hein L, Elson DS, Stoyanov D. Surgical spectral imaging. Med Image Anal 2020; 63:101699. [PMID: 32375102 PMCID: PMC7903143 DOI: 10.1016/j.media.2020.101699] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 03/30/2020] [Accepted: 04/06/2020] [Indexed: 12/24/2022]
Abstract
Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation.
Collapse
Affiliation(s)
- Neil T Clancy
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, United Kingdom; Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom.
| | - Geoffrey Jones
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, United Kingdom; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, United Kingdom
| | | | - Daniel S Elson
- Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, United Kingdom; Department of Surgery and Cancer, Imperial College London, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, United Kingdom; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, United Kingdom
| |
Collapse
|
8
|
Pre-Cancerous Stomach Lesion Detections with Multispectral-Augmented Endoscopic Prototype. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030795] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In this paper, we are interested in the in vivo detection of pre-cancerous stomach lesions. Pre-cancerous lesions are unfortunately rarely explored in research papers as most of them are focused on cancer detection or conducted ex-vivo. For this purpose, a novel prototype is introduced. It consists of a standard endoscope with multispectral cameras, an optical setup, a fiberscope, and an external light source. Reflectance spectra are acquired in vivo on 16 patients with a healthy stomach, chronic gastritis, or intestinal metaplasia. A specific pipeline has been designed for the classification of spectra between healthy mucosa and different pathologies. The pipeline includes a wavelength clustering algorithm, spectral features computation, and the training of a classifier in a “leave one patient out” manner. Good classification results, around 80%, have been obtained, and two attractive wavelength ranges were found in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm]. The new prototype and the associated results give good arguments in favor of future common use in operating rooms, during upper gastrointestinal exploration of the stomach for the detection of stomach diseases.
Collapse
|
9
|
Pardo A, Gutiérrez-Gutiérrez JA, López-Higuera JM, Conde OM. Context-free hyperspectral image enhancement for wide-field optical biomarker visualization. BIOMEDICAL OPTICS EXPRESS 2020; 11:133-148. [PMID: 32010505 PMCID: PMC6968753 DOI: 10.1364/boe.11.000133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 10/11/2019] [Accepted: 10/23/2019] [Indexed: 06/10/2023]
Abstract
Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed affinity-based color enhancement, or ACE for short, achieves multi- and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.
Collapse
Affiliation(s)
- Arturo Pardo
- Grupo Ingeniería Fotónica, dept. TEISA, Universidad de Cantabria, Avda. Los Castros S/N, 39005 Santander, Cantabria, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Cantabria, Spain
| | - José A Gutiérrez-Gutiérrez
- Grupo Ingeniería Fotónica, dept. TEISA, Universidad de Cantabria, Avda. Los Castros S/N, 39005 Santander, Cantabria, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Cantabria, Spain
| | - José M López-Higuera
- Grupo Ingeniería Fotónica, dept. TEISA, Universidad de Cantabria, Avda. Los Castros S/N, 39005 Santander, Cantabria, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Cantabria, Spain
- Biomedical Research Networking Center - Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, 28029 Madrid, Spain
| | - Olga M Conde
- Grupo Ingeniería Fotónica, dept. TEISA, Universidad de Cantabria, Avda. Los Castros S/N, 39005 Santander, Cantabria, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Cantabria, Spain
- Biomedical Research Networking Center - Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Avda. Monforte de Lemos, 3-5, Pabellón 11, Planta 0, 28029 Madrid, Spain
| |
Collapse
|
10
|
Adler TJ, Ardizzone L, Vemuri A, Ayala L, Gröhl J, Kirchner T, Wirkert S, Kruse J, Rother C, Köthe U, Maier-Hein L. Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Int J Comput Assist Radiol Surg 2019; 14:997-1007. [PMID: 30903566 DOI: 10.1007/s11548-019-01939-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 03/07/2019] [Indexed: 10/27/2022]
Abstract
PURPOSE Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to address the inverse problem of converting pixel-wise multispectral reflectance measurements to underlying tissue parameters, such as oxygenation. Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed. METHODS We present a novel approach to the assessment of optical imaging modalities, which is sensitive to the different types of uncertainties that may occur when inferring tissue parameters. Based on the concept of invertible neural networks, our framework goes beyond point estimates and maps each multispectral measurement to a full posterior probability distribution which is capable of representing ambiguity in the solution via multiple modes. Performance metrics for a hardware setup can then be computed from the characteristics of the posteriors. RESULTS Application of the assessment framework to the specific use case of camera selection for physiological parameter estimation yields the following insights: (1) estimation of tissue oxygenation from multispectral images is a well-posed problem, while (2) blood volume fraction may not be recovered without ambiguity. (3) In general, ambiguity may be reduced by increasing the number of spectral bands in the camera. CONCLUSION Our method could help to optimize optical camera design in an application-specific manner.
Collapse
Affiliation(s)
- Tim J Adler
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany. .,Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | | | - Anant Vemuri
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Leonardo Ayala
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Janek Gröhl
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.,Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Thomas Kirchner
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.,Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Sebastian Wirkert
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Jakob Kruse
- Visual Learning Lab, Heidelberg University, Heidelberg, Germany
| | - Carsten Rother
- Visual Learning Lab, Heidelberg University, Heidelberg, Germany
| | - Ullrich Köthe
- Visual Learning Lab, Heidelberg University, Heidelberg, Germany
| | - Lena Maier-Hein
- Computer Assisted Medical Interventions, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Waterhouse DJ, Luthman AS, Yoon J, Gordon GSD, Bohndiek SE. Quantitative evaluation of comb-structure correction methods for multispectral fibrescopic imaging. Sci Rep 2018; 8:17801. [PMID: 30542081 PMCID: PMC6290790 DOI: 10.1038/s41598-018-36088-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 11/13/2018] [Indexed: 02/07/2023] Open
Abstract
Removing the comb artifact introduced by imaging fibre bundles, or 'fibrescopes', for example in medical endoscopy, is essential to provide high quality images to the observer. Multispectral imaging (MSI) is an emerging method that combines morphological (spatial) and chemical (spectral) information in a single data 'cube'. When a fibrescope is coupled to a spectrally resolved detector array (SRDA) to perform MSI, comb removal is complicated by the demosaicking step required to reconstruct the multispectral data cube. To understand the potential for using SRDAs as multispectral imaging sensors in medical endoscopy, we assessed five comb correction methods with respect to five performance metrics relevant to biomedical imaging applications: processing time, resolution, smoothness, signal and the accuracy of spectral reconstruction. By assigning weights to each metric, which are determined by the particular imaging application, our results can be used to select the correction method to achieve best overall performance. In most cases, interpolation gave the best compromise between the different performance metrics when imaging using an SRDA.
Collapse
Affiliation(s)
- Dale J Waterhouse
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - A Siri Luthman
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Jonghee Yoon
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - George S D Gordon
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
| | - Sarah E Bohndiek
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK.
| |
Collapse
|
13
|
Luthman AS, Waterhouse DJ, Ansel-Bollepalli L, Yoon J, Gordon GSD, Joseph J, di Pietro M, Januszewicz W, Bohndiek SE. Bimodal reflectance and fluorescence multispectral endoscopy based on spectrally resolving detector arrays. JOURNAL OF BIOMEDICAL OPTICS 2018; 24:1-14. [PMID: 30358334 PMCID: PMC6975231 DOI: 10.1117/1.jbo.24.3.031009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 09/07/2018] [Indexed: 05/08/2023]
Abstract
Emerging clinical interest in combining standard white light endoscopy with targeted near-infrared (NIR) fluorescent contrast agents for improved early cancer detection has created demand for multimodal imaging endoscopes. We used two spectrally resolving detector arrays (SRDAs) to realize a bimodal endoscope capable of simultaneous reflectance-based imaging in the visible spectral region and multiplexed fluorescence-based imaging in the NIR. The visible SRDA was composed of 16 spectral bands, with peak wavelengths in the range of 463 to 648 nm and full-width at half-maximum (FWHM) between 9 and 26 nm. The NIR SRDA was composed of 25 spectral bands, with peak wavelengths in the range 659 to 891 nm and FWHM 7 to 15 nm. The spectral endoscope design was based on a "babyscope" model using a commercially available imaging fiber bundle. We developed a spectral transmission model to select optical components and provide reference endmembers for linear spectral unmixing of the recorded image data. The technical characterization of the spectral endoscope is presented, including evaluation of the angular field-of-view, barrel distortion, spatial resolution and spectral fidelity, which showed encouraging performance. An agarose phantom containing oxygenated and deoxygenated blood with three fluorescent dyes was then imaged. After spectral unmixing, the different chemical components of the phantom could be successfully identified via majority decision with high signal-to-background ratio (>3). Imaging performance was further assessed in an ex vivo porcine esophagus model. Our preliminary imaging results demonstrate the capability to simultaneously resolve multiple biological components using a compact spectral endoscopy system.
Collapse
Affiliation(s)
- A. Siri Luthman
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| | - Dale J. Waterhouse
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| | - Laura Ansel-Bollepalli
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| | - Jonghee Yoon
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| | - George S. D. Gordon
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Department of Engineering, Cambridge, United Kingdom
| | - James Joseph
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| | - Massimiliano di Pietro
- University of Cambridge, MRC Cancer Unit, Hutchison/MRC Research Centre, Cambridge, United Kingdom
| | - Wladyslaw Januszewicz
- University of Cambridge, MRC Cancer Unit, Hutchison/MRC Research Centre, Cambridge, United Kingdom
| | - Sarah E. Bohndiek
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Center, Robinson Way, Cambridge, United Kingdom
| |
Collapse
|