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Wang J, Wang B, Liu YY, Luo YL, Wu YY, Xiang L, Yang XM, Qu YL, Tian TR, Man Y. Recent Advances in Digital Technology in Implant Dentistry. J Dent Res 2024:220345241253794. [PMID: 38822563 DOI: 10.1177/00220345241253794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024] Open
Abstract
Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.
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Affiliation(s)
- J Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - B Wang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Sichuan, Henan
| | - Y Y Liu
- Department of Oral Implantology, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, Yunnan, Sichuan, China
| | - Y L Luo
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - L Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X M Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y L Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - T R Tian
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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Al-Otaibi S, Rehman A, Raza A, Alyami J, Saba T. CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans. PeerJ Comput Sci 2024; 10:e2008. [PMID: 38855235 PMCID: PMC11157570 DOI: 10.7717/peerj-cs.2008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/01/2024] [Indexed: 06/11/2024]
Abstract
Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.
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Affiliation(s)
- Shaha Al-Otaibi
- Department of Information Systems, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Ali Raza
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Jaber Alyami
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tanzila Saba
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
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Burström G, Amini M, El-Hajj VG, Arfan A, Gharios M, Buwaider A, Losch MS, Manni F, Edström E, Elmi-Terander A. Optical Methods for Brain Tumor Detection: A Systematic Review. J Clin Med 2024; 13:2676. [PMID: 38731204 PMCID: PMC11084501 DOI: 10.3390/jcm13092676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.
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Affiliation(s)
- Gustav Burström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Misha Amini
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Victor Gabriel El-Hajj
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Arooj Arfan
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Maria Gharios
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Ali Buwaider
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Merle S. Losch
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, 2627 Delft, The Netherlands
| | - Francesca Manni
- Department of Electrical Engineering, Eindhoven University of Technology (TU/e), 5612 Eindhoven, The Netherlands;
| | - Erik Edström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
- Department of Surgical Sciences, Uppsala University, 751 35 Uppsala, Sweden
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Anichini G, Leiloglou M, Hu Z, O'Neill K, Daniel Elson. Hyperspectral and multispectral imaging in neurosurgery: a systematic literature review and meta-analysis. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108293. [PMID: 38658267 DOI: 10.1016/j.ejso.2024.108293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/21/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic. MATERIALS AND METHODS A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted. RESULTS Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies. CONCLUSIONS The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.
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Affiliation(s)
- Giulio Anichini
- Department of Brain Sciences, Imperial College of London, United Kingdom; Department of Neurosurgery, Neuroscience, Imperial College Healthcare NHS Trust, United Kingdom.
| | - Maria Leiloglou
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
| | - Zepeng Hu
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
| | - Kevin O'Neill
- Department of Brain Sciences, Imperial College of London, United Kingdom; Department of Neurosurgery, Neuroscience, Imperial College Healthcare NHS Trust, United Kingdom
| | - Daniel Elson
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
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Jong LJS, Appelman JGC, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images. SENSORS (BASEL, SWITZERLAND) 2024; 24:1567. [PMID: 38475103 DOI: 10.3390/s24051567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
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Affiliation(s)
- Lynn-Jade S Jong
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Jelmer G C Appelman
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands
| | - Henricus J C M Sterenborg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo J M Ruers
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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Leon R, Fabelo H, Ortega S, Cruz-Guerrero IA, Campos-Delgado DU, Szolna A, Piñeiro JF, Espino C, O'Shanahan AJ, Hernandez M, Carrera D, Bisshopp S, Sosa C, Balea-Fernandez FJ, Morera J, Clavo B, Callico GM. Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection. NPJ Precis Oncol 2023; 7:119. [PMID: 37964078 PMCID: PMC10646050 DOI: 10.1038/s41698-023-00475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/24/2023] [Indexed: 11/16/2023] Open
Abstract
Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.
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Affiliation(s)
- Raquel Leon
- Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.
| | - Himar Fabelo
- Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.
- Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), Las Palmas de Gran Canaria, Spain.
| | - Samuel Ortega
- Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
- Nofima, Norwegian Institute of Food Fisheries and Aquaculture Research, Tromsø, Norway
| | - Ines A Cruz-Guerrero
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Daniel Ulises Campos-Delgado
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
- Instituto de Investigación en Comunicación Óptica, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
| | - Adam Szolna
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Juan F Piñeiro
- Instituto de Investigación en Comunicación Óptica, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
| | - Carlos Espino
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Aruma J O'Shanahan
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Maria Hernandez
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - David Carrera
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Sara Bisshopp
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Coralia Sosa
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Francisco J Balea-Fernandez
- Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
- Department of Psychology, Sociology and Social Work, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Jesus Morera
- Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Bernardino Clavo
- Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), Las Palmas de Gran Canaria, Spain
- Research Unit, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Gustavo M Callico
- Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
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Puustinen S, Hyttinen J, Elomaa AP, Vrzáková H. Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training. Data Brief 2023; 50:109526. [PMID: 37691737 PMCID: PMC10482730 DOI: 10.1016/j.dib.2023.109526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023] Open
Abstract
The dataset consists of 101 hyperspectral images of four human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers.
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Affiliation(s)
- Sami Puustinen
- Microsurgery Center of Eastern Finland, Kuopio University Hospital, Puijonlaaksontie 2, Kuopio 70210, Finland
| | - Joni Hyttinen
- Faculty of Science, Forestry and Technology, University of Eastern Finland, Yliopistokatu 2, Joensuu 80100, Finland
| | - Antti-Pekka Elomaa
- Department of Neurosurgery, Kuopio University Hospital, Puijonlaaksontie 2, Kuopio 70210, Finland
| | - Hana Vrzáková
- Faculty of Science, Forestry and Technology, University of Eastern Finland, Yliopistokatu 2, Joensuu 80100, Finland
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MacCormac O, Noonan P, Janatka M, Horgan CC, Bahl A, Qiu J, Elliot M, Trotouin T, Jacobs J, Patel S, Bergholt MS, Ashkan K, Ourselin S, Ebner M, Vercauteren T, Shapey J. Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study. Front Neurosci 2023; 17:1239764. [PMID: 37790587 PMCID: PMC10544348 DOI: 10.3389/fnins.2023.1239764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/31/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. Methods A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Results Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. Discussion This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.
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Affiliation(s)
- Oscar MacCormac
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Philip Noonan
- Hypervision Surgical Limited, London, United Kingdom
| | - Mirek Janatka
- Hypervision Surgical Limited, London, United Kingdom
| | | | - Anisha Bahl
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
| | - Jianrong Qiu
- School of Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Matthew Elliot
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Théo Trotouin
- Hypervision Surgical Limited, London, United Kingdom
| | - Jaco Jacobs
- Hypervision Surgical Limited, London, United Kingdom
| | - Sabina Patel
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Mads S. Bergholt
- School of Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
| | - Michael Ebner
- Hypervision Surgical Limited, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
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Teng G, Wang Q, Hao Q, Fan A, Yang H, Xu X, Chen G, Wei K, Zhao Z, Khan MN, Idrees BS, Bao M, Luo T, Zheng Y, Lu B. Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue. BIOMEDICAL OPTICS EXPRESS 2023; 14:3469-3490. [PMID: 37497487 PMCID: PMC10368052 DOI: 10.1364/boe.492983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/28/2023]
Abstract
The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C2 molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.
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Affiliation(s)
- Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7LD, United Kingdom
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314033, China
| | - Qun Hao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314033, China
| | - Axin Fan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Haifeng Yang
- Department of Neuro-Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Xiangjun Xu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314033, China
| | - Guoyan Chen
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Kai Wei
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhifang Zhao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - M Nouman Khan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bushra Sana Idrees
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Mengyu Bao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianzhong Luo
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314033, China
| | - Yongyue Zheng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bingheng Lu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
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10
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Kumari S, Gupta R, Ambasta RK, Kumar P. Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy. Biochim Biophys Acta Rev Cancer 2023; 1878:188913. [PMID: 37182666 DOI: 10.1016/j.bbcan.2023.188913] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/24/2023] [Accepted: 05/10/2023] [Indexed: 05/16/2023]
Abstract
Glioblastoma multiforme (GBM) is an aggressive brain cancer showing poor prognosis. Currently, treatment methods of GBM are limited with adverse outcomes and low survival rate. Thus, advancements in the treatment of GBM are of utmost importance, which can be achieved in recent decades. However, despite aggressive initial treatment, most patients develop recurrent diseases, and the overall survival rate of patients is impossible to achieve. Currently, researchers across the globe target signaling events along with tumor microenvironment (TME) through different drug molecules to inhibit the progression of GBM, but clinically they failed to demonstrate much success. Herein, we discuss the therapeutic targets and signaling cascades along with the role of the organoids model in GBM research. Moreover, we systematically review the traditional and emerging therapeutic strategies in GBM. In addition, we discuss the implications of nanotechnologies, AI, and combinatorial approach to enhance GBM therapeutics.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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11
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Puustinen S, Vrzáková H, Hyttinen J, Rauramaa T, Fält P, Hauta-Kasari M, Bednarik R, Koivisto T, Rantala S, von Und Zu Fraunberg M, Jääskeläinen JE, Elomaa AP. Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance. World Neurosurg 2023; 175:e614-e635. [PMID: 37030483 DOI: 10.1016/j.wneu.2023.03.149] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/10/2023]
Abstract
BACKGROUND Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. METHODS We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. RESULTS The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. CONCLUSIONS In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.
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Affiliation(s)
- Sami Puustinen
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland.
| | - Hana Vrzáková
- Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Joni Hyttinen
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Tuomas Rauramaa
- Kuopio University Hospital, Department of Clinical Pathology, Kuopio, Finland
| | - Pauli Fält
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Markku Hauta-Kasari
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Roman Bednarik
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Timo Koivisto
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Susanna Rantala
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Mikael von Und Zu Fraunberg
- Oulu University Hospital, Department of Neurosurgery, Oulu, Finland; University of Oulu, Faculty of Medicine, Research Unit of Clinical Medicine, Oulu, Finland
| | | | - Antti-Pekka Elomaa
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
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12
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MacCormac OJ. Letter concerning "Novel intra-operative strategies for enhancing tumour control: Future directions". Neuro Oncol 2023; 25:1196. [PMID: 37022741 PMCID: PMC10237400 DOI: 10.1093/neuonc/noad056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Affiliation(s)
- Oscar James MacCormac
- School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
- Department of Neurosurgery, King’s College Hospitals NHS Foundation Trust, London, UK
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13
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Zhang L, Huang D, Chen X, Zhu L, Xie Z, Chen X, Cui G, Zhou Y, Huang G, Shi W. Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification. JOURNAL OF BIOPHOTONICS 2023:e202300020. [PMID: 36966458 DOI: 10.1002/jbio.202300020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/07/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large-eared white rabbits were acquired using a visible near-infrared hyperspectral camera, and K-means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500-622 and 700-858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo.
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Affiliation(s)
- Lechao Zhang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Danfei Huang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Xiaojing Chen
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Libin Zhu
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhonghao Xie
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Xiaoqing Chen
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guihua Cui
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Yao Zhou
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China
| | - Guangzao Huang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
| | - Wen Shi
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China
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14
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Katsos K, Johnson SE, Ibrahim S, Bydon M. Current Applications of Machine Learning for Spinal Cord Tumors. Life (Basel) 2023; 13:life13020520. [PMID: 36836877 PMCID: PMC9962966 DOI: 10.3390/life13020520] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.
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Affiliation(s)
- Konstantinos Katsos
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sarah E. Johnson
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sufyan Ibrahim
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Mohamad Bydon
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Correspondence:
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15
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Tomanic T, Rogelj L, Stergar J, Markelc B, Bozic T, Brezar SK, Sersa G, Milanic M. Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry. JOURNAL OF BIOPHOTONICS 2023; 16:e202200181. [PMID: 36054067 DOI: 10.1002/jbio.202200181] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
Understanding tumors and their microenvironment are essential for successful and accurate disease diagnosis. Tissue physiology and morphology are altered in tumors compared to healthy tissues, and there is a need to monitor tumors and their surrounding tissues, including blood vessels, non-invasively. This preliminary study utilizes a multimodal optical imaging system combining hyperspectral imaging (HSI) and three-dimensional (3D) optical profilometry (OP) to capture hyperspectral images and surface shapes of subcutaneously grown murine tumor models. Hyperspectral images are corrected with 3D OP data and analyzed using the inverse-adding doubling (IAD) method to extract tissue properties such as melanin volume fraction and oxygenation. Blood vessels are segmented using the B-COSFIRE algorithm from oxygenation maps. From 3D OP data, tumor volumes are calculated and compared to manual measurements using a vernier caliper. Results show that tumors can be distinguished from healthy tissue based on most extracted tissue parameters ( p < 0.05 ). Furthermore, blood oxygenation is 50% higher within the blood vessels than in the surrounding tissue, and tumor volumes calculated using 3D OP agree within 26% with manual measurements using a vernier caliper. Results suggest that combining HSI and OP could provide relevant quantitative information about tumors and improve the disease diagnosis.
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Affiliation(s)
- Tadej Tomanic
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Luka Rogelj
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Jost Stergar
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jozef Stefan Institute, Ljubljana, Slovenia
| | - Bostjan Markelc
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
- Faculty of Health Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Tim Bozic
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Simona Kranjc Brezar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Gregor Sersa
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
- Faculty of Health Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Matija Milanic
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jozef Stefan Institute, Ljubljana, Slovenia
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16
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Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning. Cancers (Basel) 2022; 15:cancers15010213. [PMID: 36612208 PMCID: PMC9818424 DOI: 10.3390/cancers15010213] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/16/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment.
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17
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Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249790. [PMID: 36560157 PMCID: PMC9784550 DOI: 10.3390/s22249790] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 06/13/2023]
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
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Affiliation(s)
- Rong Cui
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - He Yu
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Tingfa Xu
- Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Xiaoxue Xing
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Xiaorui Cao
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Kang Yan
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Jiexi Chen
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
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18
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Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review. Radiol Oncol 2022; 56:420-429. [PMID: 36503709 PMCID: PMC9784371 DOI: 10.2478/raon-2022-0051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Hyperspectral imaging (HSI) is a promising imaging modality that uses visible light to obtain information about blood flow. It has the distinct advantage of being noncontact, nonionizing, and noninvasive without the need for a contrast agent. Among the many applications of HSI in the medical field are the detection of various types of tumors and the evaluation of their blood flow, as well as the healing processes of grafts and wounds. Since tumor perfusion is one of the critical factors in oncology, we assessed the value of HSI in quantifying perfusion changes during interventions in clinical oncology through a systematic review of the literature. MATERIALS AND METHODS The PubMed and Web of Science electronic databases were searched using the terms "hyperspectral imaging perfusion cancer" and "hyperspectral imaging resection cancer". The inclusion criterion was the use of HSI in clinical oncology, meaning that all animal, phantom, ex vivo, experimental, research and development, and purely methodological studies were excluded. RESULTS Twenty articles met the inclusion criteria. The anatomic locations of the neoplasms in the selected articles were as follows: kidneys (1 article), breasts (2 articles), eye (1 article), brain (4 articles), entire gastrointestinal (GI) tract (1 article), upper GI tract (5 articles), and lower GI tract (6 articles). CONCLUSIONS HSI is a potentially attractive imaging modality for clinical application in oncology, with assessment of mastectomy skin flap perfusion after reconstructive breast surgery and anastomotic perfusion during reconstruction of gastrointenstinal conduit as the most promising at present.
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19
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Martinez-Vega B, Tkachenko M, Matkabi M, Ortega S, Fabelo H, Balea-Fernandez F, La Salvia M, Torti E, Leporati F, Callico GM, Chalopin C. Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8917. [PMID: 36433516 PMCID: PMC9693077 DOI: 10.3390/s22228917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.
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Affiliation(s)
- Beatriz Martinez-Vega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Mariia Tkachenko
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), University of Leipzig, 04105 Leipzig, Germany
| | - Marianne Matkabi
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
- Department of Electrical Engineering, Mechanical Engineering and Industrial Engineering, Anhalt University of Applied Science Anhalt, 06366 Köthen, Germany
| | - Samuel Ortega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Nofima, Norwegian Institute of Food Fisheries and Aquaculture Research, NO-9291 Tromsø, Norway
| | - Himar Fabelo
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Fundacion Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), 35019 Las Palmas de Gran Canaria, Spain
| | - Francisco Balea-Fernandez
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Department of Psychology, Sociology and Social Work, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Marco La Salvia
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Emanuele Torti
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Francesco Leporati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy
| | - Gustavo M. Callico
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Claire Chalopin
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
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20
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Van Hese L, De Vleeschouwer S, Theys T, Rex S, Heeren RMA, Cuypers E. The diagnostic accuracy of intraoperative differentiation and delineation techniques in brain tumours. Discov Oncol 2022; 13:123. [PMID: 36355227 PMCID: PMC9649524 DOI: 10.1007/s12672-022-00585-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/22/2022] [Indexed: 11/11/2022] Open
Abstract
Brain tumour identification and delineation in a timeframe of seconds would significantly guide and support surgical decisions. Here, treatment is often complicated by the infiltration of gliomas in the surrounding brain parenchyma. Accurate delineation of the invasive margins is essential to increase the extent of resection and to avoid postoperative neurological deficits. Currently, histopathological annotation of brain biopsies and genetic phenotyping still define the first line treatment, where results become only available after surgery. Furthermore, adjuvant techniques to improve intraoperative visualisation of the tumour tissue have been developed and validated. In this review, we focused on the sensitivity and specificity of conventional techniques to characterise the tumour type and margin, specifically fluorescent-guided surgery, neuronavigation and intraoperative imaging as well as on more experimental techniques such as mass spectrometry-based diagnostics, Raman spectrometry and hyperspectral imaging. Based on our findings, all investigated methods had their advantages and limitations, guiding researchers towards the combined use of intraoperative imaging techniques. This can lead to an improved outcome in terms of extent of tumour resection and progression free survival while preserving neurological outcome of the patients.
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Affiliation(s)
- Laura Van Hese
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
- Department of Anaesthesiology, University Hospitals Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium
| | - Steven De Vleeschouwer
- Neurosurgery Department, University Hospitals Leuven, 3000, Leuven, Belgium
- Laboratory for Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, Leuven Brain Institute (LBI), 3000, Leuven, Belgium
| | - Tom Theys
- Neurosurgery Department, University Hospitals Leuven, 3000, Leuven, Belgium
- Laboratory for Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, Leuven Brain Institute (LBI), 3000, Leuven, Belgium
| | - Steffen Rex
- Department of Anaesthesiology, University Hospitals Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium
| | - Ron M A Heeren
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - Eva Cuypers
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
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21
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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: 0] [Impact Index Per Article: 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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22
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Massalimova A, Timmermans M, Esfandiari H, Carrillo F, Laux CJ, Farshad M, Denis K, Fürnstahl P. Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review. Front Surg 2022; 9:952539. [PMID: 35990097 PMCID: PMC9381957 DOI: 10.3389/fsurg.2022.952539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.
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Affiliation(s)
- Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
- Correspondence: Aidana Massalimova
| | - Maikel Timmermans
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Hooman Esfandiari
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Christoph J. Laux
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Kathleen Denis
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
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23
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Wu Y, Xu Z, Yang W, Ning Z, Dong H. Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery. Front Bioeng Biotechnol 2022; 10:906728. [PMID: 35711634 PMCID: PMC9196632 DOI: 10.3389/fbioe.2022.906728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.
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Affiliation(s)
- Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhiqiang Ning
- Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS), Hefei, China.,Science Island Branch, Graduate School of USTC, Hefei, China
| | - Hao Dong
- Research Center for Sensing Materials and Devices, Zhejiang Lab, Hangzhou, China
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24
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van Vliet-Pérez SM, van de Berg NJ, Manni F, Lai M, Rijstenberg L, Hendriks BHW, Dankelman J, Ewing-Graham PC, Nieuwenhuyzen-de Boer GM, van Beekhuizen HJ. Hyperspectral Imaging for Tissue Classification after Advanced Stage Ovarian Cancer Surgery-A Pilot Study. Cancers (Basel) 2022; 14:cancers14061422. [PMID: 35326577 PMCID: PMC8946803 DOI: 10.3390/cancers14061422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/24/2022] [Accepted: 03/08/2022] [Indexed: 02/05/2023] Open
Abstract
The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665−975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.
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Affiliation(s)
- Sharline M. van Vliet-Pérez
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (N.J.v.d.B.); (B.H.W.H.); (J.D.)
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Correspondence:
| | - Nick J. van de Berg
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (N.J.v.d.B.); (B.H.W.H.); (J.D.)
- Department of Gynecological Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.M.N.-d.B.); (H.J.v.B.)
| | - Francesca Manni
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (F.M.); (M.L.)
| | - Marco Lai
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (F.M.); (M.L.)
| | - Lucia Rijstenberg
- Department of Pathology, Erasmus University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (L.R.); (P.C.E.-G.)
| | - Benno H. W. Hendriks
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (N.J.v.d.B.); (B.H.W.H.); (J.D.)
| | - Jenny Dankelman
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (N.J.v.d.B.); (B.H.W.H.); (J.D.)
| | - Patricia C. Ewing-Graham
- Department of Pathology, Erasmus University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (L.R.); (P.C.E.-G.)
| | - Gatske M. Nieuwenhuyzen-de Boer
- Department of Gynecological Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.M.N.-d.B.); (H.J.v.B.)
- Department of Gynecology, Albert Schweitzer Hospital, 3318 AT Dordrecht, The Netherlands
| | - Heleen J. van Beekhuizen
- Department of Gynecological Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.M.N.-d.B.); (H.J.v.B.)
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25
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Eggert D, Bengs M, Westermann S, Gessert N, Gerstner AOH, Mueller NA, Bewarder J, Schlaefer A, Betz C, Laffers W. In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods. JOURNAL OF BIOPHOTONICS 2022; 15:e202100167. [PMID: 34889065 DOI: 10.1002/jbio.202100167] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/25/2021] [Accepted: 11/25/2021] [Indexed: 05/24/2023]
Abstract
Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81%, a sensitivity of 83% and a specificity of 79%.
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Affiliation(s)
- Dennis Eggert
- Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marcel Bengs
- Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany
| | - Stephan Westermann
- Department of Otorhinolaryngology/Head and Neck Surgery, University of Bonn, Bonn, Germany
| | - Nils Gessert
- Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany
| | | | - Nina A Mueller
- Department of Otorhinolaryngology/Head and Neck Surgery, University of Bonn, Bonn, Germany
| | - Julian Bewarder
- Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Schlaefer
- Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany
| | - Christian Betz
- Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Wiebke Laffers
- Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Otorhinolaryngology/Head and Neck Surgery, University of Bonn, Bonn, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, Evangelisches Krankenhaus, Carl von Ossietzky-University, Oldenburg, Germany
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26
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Chen K, Li H, Pan Z, Wu Z, Song E. Insights into artificial intelligence in clinical oncology: opportunities and challenges. SCIENCE CHINA. LIFE SCIENCES 2022; 65:643-647. [PMID: 34846642 DOI: 10.1007/s11427-021-2010-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/18/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Hanwei Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhanpeng Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhuo Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Fountain-Valley Institute for Life Sciences, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Bioland Laboratory, Guangzhou, 510005, China.
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27
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Evaluation of Hyperspectral Imaging for Follow-Up Assessment after Revascularization in Peripheral Artery Disease. J Clin Med 2022; 11:jcm11030758. [PMID: 35160210 PMCID: PMC8836513 DOI: 10.3390/jcm11030758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Assessment of tissue oxygenation is an important aspect of detection and monitoring of patients with peripheral artery disease (PAD). Hyperspectral imaging (HSI) is a non-contact technology for assessing microcirculatory function by quantifying tissue oxygen saturation (StO2). This study investigated whether HSI can be used to monitor skin oxygenation in patients with PAD after appropriate treatment of the lower extremities. Methods: For this purpose, 37 patients with PAD were studied by means of ankle–brachial index (ABI) and HSI before and after surgical or endovascular therapy. Thereby, the oxygenation parameter StO2 and near infrared (NIR) perfusion index were quantified in seven angiosomes on the diseased lower leg and foot. In addition, the effects of skin temperature and physical activity on StO2 and the NIR perfusion index and the respective inter-operator variability of these parameters were investigated in 25 healthy volunteers. Results: In all patients, the ABI significantly increased after surgical and endovascular therapy. In parallel, HSI revealed significant changes in both StO2 and NIR perfusion index in almost all studied angiosomes depending on the performed treatment. The increase in tissue oxygenation saturation was especially pronounced after surgical treatment. Neither heat nor cold, nor physical activity, nor repeated assessments of HSI parameters by independent investigators significantly affected the results on StO2 and the NIR perfusion index. Conclusions: Tissue oxygen saturation data obtained with HSI are robust to external confounders, such as temperature and physical activity, and do not show inter-operator variability; therefore, can be used as an additional technique to established methods, such as the ABI, to monitor peripheral perfusion in patients with PAD.
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Abstract
AbstractMeasuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.
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29
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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30
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Tukra S, Lidströmer N, Ashrafian H, Gianarrou S. AI in Surgical Robotics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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31
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Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:349-361. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
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32
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Barberio M, Benedicenti S, Pizzicannella M, Felli E, Collins T, Jansen-Winkeln B, Marescaux J, Viola MG, Diana M. Intraoperative Guidance Using Hyperspectral Imaging: A Review for Surgeons. Diagnostics (Basel) 2021; 11:diagnostics11112066. [PMID: 34829413 PMCID: PMC8624094 DOI: 10.3390/diagnostics11112066] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 12/12/2022] Open
Abstract
Hyperspectral imaging (HSI) is a novel optical imaging modality, which has recently found diverse applications in the medical field. HSI is a hybrid imaging modality, combining a digital photographic camera with a spectrographic unit, and it allows for a contactless and non-destructive biochemical analysis of living tissue. HSI provides quantitative and qualitative information of the tissue composition at molecular level in a contrast-free manner, hence making it possible to objectively discriminate between different tissue types and between healthy and pathological tissue. Over the last two decades, HSI has been increasingly used in the medical field, and only recently it has found an application in the operating room. In the last few years, several research groups have used this imaging modality as an intraoperative guidance tool within different surgical disciplines. Despite its great potential, HSI still remains far from being routinely used in the daily surgical practice, since it is still largely unknown to most of the surgical community. The aim of this study is to provide clinical surgeons with an overview of the capabilities, current limitations, and future directions of HSI for intraoperative guidance.
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Affiliation(s)
- Manuel Barberio
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France; (T.C.); (J.M.); (M.D.)
- General Surgery Department, Ospedale Card. G. Panico, 73039 Tricase, Italy; (S.B.); (M.P.); (M.G.V.)
- Correspondence:
| | - Sara Benedicenti
- General Surgery Department, Ospedale Card. G. Panico, 73039 Tricase, Italy; (S.B.); (M.P.); (M.G.V.)
| | - Margherita Pizzicannella
- General Surgery Department, Ospedale Card. G. Panico, 73039 Tricase, Italy; (S.B.); (M.P.); (M.G.V.)
| | - Eric Felli
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, 3008 Bern, Switzerland;
- Department for BioMedical Research, Visceral Surgery and Medicine, University of Bern, 3008 Bern, Switzerland
| | - Toby Collins
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France; (T.C.); (J.M.); (M.D.)
| | | | - Jacques Marescaux
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France; (T.C.); (J.M.); (M.D.)
| | - Massimo Giuseppe Viola
- General Surgery Department, Ospedale Card. G. Panico, 73039 Tricase, Italy; (S.B.); (M.P.); (M.G.V.)
| | - Michele Diana
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France; (T.C.); (J.M.); (M.D.)
- ICube Laboratory, Photonics Instrumentation for Health, University of Strasbourg, 67400 Strasbourg, France
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Baig N, Fabelo H, Ortega S, Callico GM, Alirezaie J, Umapathy K. Empirical Mode Decomposition Based Hyperspectral Data Analysis for Brain Tumor Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2274-2277. [PMID: 34891740 DOI: 10.1109/embc46164.2021.9629676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The capability of Hyperspectral Imaging (HSI) in rapidly acquiring abundant reflectance data in a non-invasive manner, makes it an ideal tool for obtaining diagnostic information about tissue pathology. Identifying wavelengths that provide the most discriminatory clues for specific pathologies will greatly assist in understanding their underlying biochemical characteristics. In this paper, we propose an efficient and computationally inexpensive method for determining the most relevant spectral bands for brain tumor classification. Empirical mode decomposition was used in combination with extrema analysis to extract the relevant bands based on the morphological characteristics of the spectra. The results of our experiments indicate that the proposed method outperforms the benchmark in reducing computational complexity while performing comparably with a 7-times reduction in the feature-set for classification on the test data.
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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Leon R, Fabelo H, Ortega S, Piñeiro JF, Szolna A, Hernandez M, Espino C, O'Shanahan AJ, Carrera D, Bisshopp S, Sosa C, Marquez M, Morera J, Clavo B, Callico GM. VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection. Sci Rep 2021; 11:19696. [PMID: 34608237 PMCID: PMC8490425 DOI: 10.1038/s41598-021-99220-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 09/16/2021] [Indexed: 12/25/2022] Open
Abstract
Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.
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Affiliation(s)
- Raquel Leon
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017, Las Palmas de Gran Canaria, Spain.
| | - Himar Fabelo
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017, Las Palmas de Gran Canaria, Spain.
| | - Samuel Ortega
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017, Las Palmas de Gran Canaria, Spain.,Nofima, Norwegian Institute of Food Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 6122, NO-9291, Tromsø, Norway
| | - Juan F Piñeiro
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Adam Szolna
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Maria Hernandez
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Carlos Espino
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Aruma J O'Shanahan
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - David Carrera
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Sara Bisshopp
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Coralia Sosa
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Mariano Marquez
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Jesus Morera
- Department of Neurosurgery, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Bernardino Clavo
- Research Unit, Instituto de Investigación Sanitaria de Canarias (IISC), University Hospital Doctor Negrin of Gran Canaria, Barranco de la Ballena S/N, 35010, Las Palmas de Gran Canaria, Spain
| | - Gustavo M Callico
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017, Las Palmas de Gran Canaria, Spain.
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Sethy PK, Behera SK. A data constrained approach for brain tumour detection using fused deep features and SVM. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:28745-28760. [DOI: 10.1007/s11042-021-11098-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/14/2021] [Accepted: 05/21/2021] [Indexed: 08/02/2023]
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37
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Michael Ebner, Nabavi E, Shapey J, Xie Y, Liebmann F, Spirig JM, Hoch A, Farshad M, Saeed SR, Bradford R, Yardley I, Ourselin S, Edwards AD, Führnstahl P, Vercauteren T. Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation. JOURNAL OF PHYSICS D: APPLIED PHYSICS 2021; 54:294003. [PMID: 34024940 PMCID: PMC8132621 DOI: 10.1088/1361-6463/abfbf6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/30/2021] [Accepted: 04/27/2021] [Indexed: 10/05/2023]
Abstract
Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.
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Affiliation(s)
- Michael Ebner
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Eli Nabavi
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, UCL, London, United Kingdom
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Yijing Xie
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Florentin Liebmann
- Research in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland
- Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland
| | - José Miguel Spirig
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Armando Hoch
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
- The Ear Institute, UCL, London, United Kingdom
- The Royal National Throat, Nose and Ear Hospital, London, United Kingdom
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Iain Yardley
- Department of Paediatric Surgery, Evelina London Children’s Hospital, London, United Kingdom
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - A David Edwards
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Department of Paediatric Surgery, Evelina London Children’s Hospital, London, United Kingdom
| | - Philipp Führnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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38
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Wang Q, Wang J, Zhou M, Li Q, Wen Y, Chu J. A 3D attention networks for classification of white blood cells from microscopy hyperspectral images. OPTICS & LASER TECHNOLOGY 2021; 139:106931. [DOI: 10.1016/j.optlastec.2021.106931] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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39
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Tu T, Wei X, Yang Y, Zhang N, Li W, Tu X, Li W. Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery. BMC Nephrol 2021; 22:231. [PMID: 34147076 PMCID: PMC8214276 DOI: 10.1186/s12882-021-02421-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/03/2021] [Indexed: 11/10/2022] Open
Abstract
Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.
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Affiliation(s)
- Tianqi Tu
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.,Department of Nephrology, China-Japan Friendship Hospital, Beijing, China
| | - Xueling Wei
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Yue Yang
- Department of Nephrology, China-Japan Friendship Hospital, Beijing, China
| | - Nianrong Zhang
- Department of Nephrology, China-Japan Friendship Hospital, Beijing, China
| | - Wei Li
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Xiaowen Tu
- Department of Nephrology, PLA Rocket Force Characteristic Medical Center, Beijing, China.
| | - Wenge Li
- Department of Nephrology, China-Japan Friendship Hospital, Beijing, China.
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40
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Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. SENSORS 2021; 21:s21113827. [PMID: 34073145 PMCID: PMC8199064 DOI: 10.3390/s21113827] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 01/29/2023]
Abstract
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.
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Affiliation(s)
- Gemma Urbanos
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Alberto Martín
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Guillermo Vázquez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Marta Villanueva
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Manuel Villa
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Luis Jimenez-Roldan
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Miguel Chavarrías
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Correspondence:
| | - Alfonso Lagares
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Eduardo Juárez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - César Sanz
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
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Shabestri B, Anastasio MA, Fei B, Leblond F. Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-21-0414. [PMID: 33973425 PMCID: PMC8109026 DOI: 10.1117/1.jbo.26.5.052901] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 01/01/2021] [Indexed: 06/12/2023]
Abstract
Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics.
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Affiliation(s)
- Behrouz Shabestri
- National Institute of Biomedical Imaging and Bioengineering, Maryland, United States
| | | | - Baowei Fei
- University of Texas at Dallas, Texas, United States
- UT Southwestern Medical Center, Texas United States
| | - Frédéric Leblond
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
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Daisy PS, Anitha TS. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy? Med Oncol 2021; 38:53. [PMID: 33811540 DOI: 10.1007/s12032-021-01500-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/20/2021] [Indexed: 12/17/2022]
Abstract
Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviour of gliomas is mainly attributed to their rapid proliferation, unravelled genomics and the blood-brain barrier which protects the tumor cells from chemotherapeutic regimens. Suspects of brain tumors are usually assessed by magnetic resonance imaging and computed tomography. These images allow surgeons to decide on the tumor grading, intra-operative pathology, feasibility of surgery, and treatment planning. All these data are compiled manually by physicians, wherein it takes time for the validation of results and concluding the treatment modality. In this context, the arrival of artificial intelligence in this era of personalized medicine, has proven promising performance in the diagnosis and management of gliomas. Starting from grading prediction till outcome evaluation, artificial intelligence-based forefronts have revolutionized oncological research. Interestingly, this approach has also been able to precisely differentiate tumor lesion from healthy tissues. However, till date, their utility in neuro-oncological field remains limited due to the issues pertaining to their reliability and transparency. Hence, to shed novel insights on the "clinical utility of this novel approach on glioma management" and to reveal "the black-boxes that have to be solved for fruitful application of artificial intelligence in neuro-oncology research", we provide in this review, a succinct description of the potential gear of artificial intelligence-based avenues in glioma treatment and the barriers that impede their rapid implementation in neuro-oncology.
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Affiliation(s)
- Precilla S Daisy
- Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India
| | - T S Anitha
- Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India. .,Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth, Mahatma Gandhi Medical College and Research Institute Campus, Pillaiyarkuppam, Puducherry, 607403, India.
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43
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Jansen-Winkeln B, Barberio M, Chalopin C, Schierle K, Diana M, Köhler H, Gockel I, Maktabi M. Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy. Cancers (Basel) 2021; 13:cancers13050967. [PMID: 33669082 PMCID: PMC7956537 DOI: 10.3390/cancers13050967] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/18/2021] [Accepted: 02/20/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Detection of colorectal carcinoma is performed visually by investigators and is confirmed pathologically. With hyperspectral imaging, an expanded spectral range of optical information is now available for analysis. The acquired recordings were analyzed with a neural network, and it was possible to differentiate tumor from healthy mucosa in colorectal carcinoma by automatic classification with high reliability. Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. This is a step towards optical biopsy. Abstract Currently, colorectal cancer (CRC) is mainly identified via a visual assessment during colonoscopy, increasingly used artificial intelligence algorithms, or surgery. Subsequently, CRC is confirmed through a histopathological examination by a pathologist. Hyperspectral imaging (HSI), a non-invasive optical imaging technology, has shown promising results in the medical field. In the current study, we combined HSI with several artificial intelligence algorithms to discriminate CRC. Between July 2019 and May 2020, 54 consecutive patients undergoing colorectal resections for CRC were included. The tumor was imaged from the mucosal side with a hyperspectral camera. The image annotations were classified into three groups (cancer, CA; adenomatous margin around the central tumor, AD; and healthy mucosa, HM). Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. Hyperspectral imaging combined with automatic classification can be used to differentiate between CRC and healthy mucosa. Additionally, the biological changes induced by chemotherapy to the tissue are detectable with HSI.
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Affiliation(s)
- Boris Jansen-Winkeln
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, 04103 Leipzig, Germany; (M.B.); (I.G.)
- Correspondence: ; Tel.: +49-341-9717211; Fax: +49-341-9728167
| | - Manuel Barberio
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, 04103 Leipzig, Germany; (M.B.); (I.G.)
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France;
- Department of General Surgery, Hospital Card. G. Panico, 73039 Tricase, Italy
| | - Claire Chalopin
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany; (C.C.); (H.K.); (M.M.)
| | - Katrin Schierle
- Institute of Pathology, University Hospital Leipzig, 04103 Leipzig, Germany;
| | - Michele Diana
- Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France;
| | - Hannes Köhler
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany; (C.C.); (H.K.); (M.M.)
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, 04103 Leipzig, Germany; (M.B.); (I.G.)
| | - Marianne Maktabi
- Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany; (C.C.); (H.K.); (M.M.)
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Tukra S, Lidströmer N, Ashrafian H, Giannarou S. AI in Surgical Robotics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_323-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Ranti D, Valliani AAA, Costa A, Oermann EK. Artificial intelligence as applied to clinical neurological conditions. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00020-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wang Q, Sun L, Wang Y, Zhou M, Hu M, Chen J, Wen Y, Li Q. Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:218-227. [PMID: 32956043 DOI: 10.1109/tmi.2020.3024923] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Skin biopsy histopathological analysis is one of the primary methods used for pathologists to assess the presence and deterioration of melanoma in clinical. A comprehensive and reliable pathological analysis is the result of correctly segmented melanoma and its interaction with benign tissues, and therefore providing accurate therapy. In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net with the modified loss function. These findings demonstrated the ability of the Hyper-net for assisting pathologists in diagnosis of melanoma based on hyperspectral pathology images.
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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: 31] [Impact Index Per Article: 10.3] [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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Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O. Deep learning for brain disorders: from data processing to disease treatment. Brief Bioinform 2020; 22:1560-1576. [PMID: 33316030 DOI: 10.1093/bib/bbaa310] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.
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Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging. SENSORS 2020; 20:s20247120. [PMID: 33322543 PMCID: PMC7764605 DOI: 10.3390/s20247120] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/07/2020] [Accepted: 12/09/2020] [Indexed: 11/16/2022]
Abstract
Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences.
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