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Courtenay LA, Barbero-García I, Martínez-Lastras S, Del Pozo S, Corral M, González-Aguilera D. Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification. Photodiagnosis Photodyn Ther 2024; 49:104269. [PMID: 39002835 DOI: 10.1016/j.pdpdt.2024.104269] [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: 06/07/2024] [Revised: 06/29/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND The early detection of Non-Melanoma Skin Cancer (NMSC) is essential to ensure patients receive the most effective treatment. Diagnostic screening tools for NMSC are crucial due to high confusion rates with other types of skin lesions, such as Actinic Keratosis. Nevertheless, current means of diagnosing and screening patients rely on either visual criteria, that are often conditioned by subjectivity and experience, or highly invasive, slow, and costly methods, such as histological diagnoses. From this, the objectives of the present study are to test if classification accuracies improve in the Near-Infrared region of the electromagnetic spectrum, as opposed to previous research in shorter wavelengths. METHODS This study utilizes near-infrared hyperspectral imaging, within the range of 900.6 and 1454.8 nm. Images were captured for a total of 125 patients, including 66 patients with Basal Cell Carcinoma, 42 with cutaneous Squamous Cell Carcinoma, and 17 with Actinic Keratosis, to differentiate between healthy and unhealthy skin lesions. A combination of hybrid convolutional neural networks (for feature extraction) and support vector machine algorithms (as a final activation layer) was employed for analysis. In addition, we test whether transfer learning is feasible from networks trained on shorter wavelengths of the electromagnetic spectrum. RESULTS The implemented method achieved a general accuracy of over 80 %, with some tasks reaching over 90 %. F1 scores were also found to generally be over the optimal threshold of 0.8. The best results were obtained when detecting Actinic Keratosis, however differentiation between the two types of malignant lesions was often noted to be more difficult. These results demonstrate the potential of near-infrared hyperspectral imaging combined with advanced machine learning techniques in distinguishing NMSC from other skin lesions. Transfer learning was unsuccessful in improving the training of these algorithms. CONCLUSIONS We have shown that the Near-Infrared region of the electromagnetic spectrum is highly useful for the identification and study of non-melanoma type skin lesions. While the results are promising, further research is required to develop more robust algorithms that can minimize the impact of noise in these datasets before clinical application is feasible.
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Affiliation(s)
- Lloyd A Courtenay
- CNRS, PACEA UMR 5199, Université de Bordeaux, Bât B2, Allée Geoffroy Saint Hilaire, CS50023, Pessac, 33600, France.
| | - Inés Barbero-García
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Saray Martínez-Lastras
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Susana Del Pozo
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
| | - Miriam Corral
- Dermatology Service, Ávila Healthcare Complex, Calle Jesús del Gran Poder 42, 05003, Ávila, Spain
| | - Diego González-Aguilera
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
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Courtenay LA, Barbero-García I, Martínez-Lastras S, Del Pozo S, Corral de la Calle M, Garrido A, Guerrero-Sevilla D, Hernandez-Lopez D, González-Aguilera D. Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation. PLoS One 2024; 19:e0300400. [PMID: 38662718 PMCID: PMC11045066 DOI: 10.1371/journal.pone.0300400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/26/2024] [Indexed: 04/28/2024] Open
Abstract
One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66-1085.38 nm, 1109.06-1208.53 nm, 1236.95-1322.21 nm, and 1383.79-1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.
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Affiliation(s)
- Lloyd A. Courtenay
- CNRS, PACEA UMR 5199, Université de Bordeaux, Bât B2, Pessac, 33600, France
| | - Inés Barbero-García
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
| | - Saray Martínez-Lastras
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
| | - Susana Del Pozo
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
| | | | - Alonso Garrido
- Institute of Regional Development, University of Castilla la Mancha, Campus Universitario s/n, Albacete, Spain
| | - Diego Guerrero-Sevilla
- Institute of Regional Development, University of Castilla la Mancha, Campus Universitario s/n, Albacete, Spain
| | - David Hernandez-Lopez
- Institute of Regional Development, University of Castilla la Mancha, Campus Universitario s/n, Albacete, Spain
| | - Diego González-Aguilera
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
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Aramendi J, Mabulla A, Baquedano E, Domínguez-Rodrigo M. Biomechanical and taxonomic diversity in the Early Pleistocene in East Africa: Structural analysis of a recently discovered femur shaft from Olduvai Gorge (bed I). J Hum Evol 2024; 186:103469. [PMID: 38071888 DOI: 10.1016/j.jhevol.2023.103469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 12/30/2023]
Abstract
Recent Plio-Pleistocene hominin findings have revealed the complexity of human evolutionary history and the difficulties involved in its interpretation. Moreover, the study of hominin long bone remains is particularly problematic, since it commonly depends on the analysis of fragmentary skeletal elements that in many cases are merely represented by small diaphyseal portions and appear in an isolated fashion in the fossil record. Nevertheless, the study of the postcranial skeleton is particularly important to ascertain locomotor patterns. Here we report on the discovery of a robust hominin femoral fragment (OH 84) at the site of Amin Mturi Korongo dated to 1.84 Ma (Olduvai Bed I). External anatomy and internal bone structure of OH 84 were analyzed and compared with previously published data for modern humans and chimpanzees, as well as for Australopithecus, Paranthropus and Homo specimens ranging from the Late Pliocene to Late Pleistocene. Biomechanical analyses based on transverse cross-sections and the comparison of OH 84 with another robust Olduvai specimen (OH 80) suggest that OH 84 might be tentatively allocated to Paranthropus boisei. More importantly, the identification of a unique combination of traits in OH 84 could indicate both terrestrial bipedalism and an arboreal component in the locomotor repertoire of this individual. If interpreted correctly, OH 84 could thus add to the already mounting evidence of substantial locomotor diversity among Early Pleistocene hominins. Likewise, our results also highlight the difficulties in accurately interpreting the link between form and function in the human fossil record based on fragmentary remains, and ultimately in distinguishing between coeval hominin groups due to the heterogeneous pattern of inter- and intraspecific morphological variability detected among fossil femora.
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Affiliation(s)
- Julia Aramendi
- McDonald Institute for Archaeological Research, University of Cambridge, CB2 1TN, UK.
| | - Audax Mabulla
- Department of Archaeology and Heritage Studies, University of Dar Es Salaam, P.O. Box 35050, Dar Es Salaam, Tanzania
| | - Enrique Baquedano
- Archaeological and Paleontological Museum of the Community of Madrid, Plaza de Las Bernardas s/n, 28801, Alcalá de Henares, Spain; Institute of Evolution in Africa (IDEA), University of Alcalá and Archaeological and Paleontological Museum of the Community of Madrid, C/Covarrubias 36, 28010, Madrid, Spain
| | - Manuel Domínguez-Rodrigo
- Institute of Evolution in Africa (IDEA), University of Alcalá and Archaeological and Paleontological Museum of the Community of Madrid, C/Covarrubias 36, 28010, Madrid, Spain; University of Alcalá, Department of History and Philosophy, Area of Prehistory, C/Colegios 2, 28801, Alcalá de Henares, Spain; Rice University, Department of Anthropology, 6100 Main St., Houston, TX, 77005 1827, USA
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Huang HY, Hsiao YP, Karmakar R, Mukundan A, Chaudhary P, Hsieh SC, Wang HC. A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer. Cancers (Basel) 2023; 15:5634. [PMID: 38067338 PMCID: PMC10705122 DOI: 10.3390/cancers15235634] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 08/15/2024] Open
Abstract
Skin cancer, a malignant neoplasm originating from skin cell types including keratinocytes, melanocytes, and sweat glands, comprises three primary forms: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and malignant melanoma (MM). BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity.
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Affiliation(s)
- Hung-Yi Huang
- Department of Dermatology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan;
| | - Yu-Ping Hsiao
- Department of Dermatology, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South District, Taichung City 40201, Taiwan;
- Institute of Medicine, School of Medicine, Chung Shan Medical University, No.110, Sec. 1, Jianguo N. Rd., South District, Taichung City 40201, Taiwan
| | - Riya Karmakar
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan; (R.K.); (A.M.)
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan; (R.K.); (A.M.)
| | - Pramod Chaudhary
- Department of Aeronautical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600 062, India;
| | - Shang-Chin Hsieh
- Department of Plastic Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung 80284, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan; (R.K.); (A.M.)
- Department of Medical Research, Dalin Tzu Chi General Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chia Yi City 62247, Taiwan
- Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung 80661, Taiwan
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Yeh YW, Hsu TW, Su YH, Wang CH, Liao PH, Chiu CF, Tseng PC, Chen TM, Lee WR, Tzeng YS. Silencing of Dicer enhances dacarbazine resistance in melanoma cells by inhibiting ADSL expression. Aging (Albany NY) 2023; 15:12873-12889. [PMID: 37976135 DOI: 10.18632/aging.205207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/15/2023] [Indexed: 11/19/2023]
Abstract
Dacarbazine (DTIC) is the primary first-line treatment for advanced-stage metastatic melanoma; thus, DTIC resistance is poses a major challenge. Therefore, investigating the mechanism underlying DTIC resistance must be investigated. Dicer, a type III cytoplasmic endoribonuclease, plays a pivotal role in the maturation of miRNAs. Aberrant Dicer expression may contribute to tumor progression, clinical aggressiveness, and poor prognosis in various tumors. Dicer inhibition led to a reduction in DTIC sensitivity and an augmentation in stemness in melanoma cells. Clinical analyses indicated a low Dicer expression level as a predictor of poor prognosis factor. Metabolic alterations in tumor cells may interfere with drug response. Adenylosuccinate lyase (ADSL) is a crucial enzyme in the purine metabolism pathway. An imbalance in ADSL may interfere with the therapeutic efficacy of drugs. We discovered that DTIC treatment enhanced ADSL expression and that Dicer silencing significantly reduced ADSL expression in melanoma cells. Furthermore, ADSL overexpression reversed Dicer silencing induced DTIC resistance and cancer stemness. These findings indicate that Dicer-mediated ADSL regulation influences DTIC sensitivity and stemness in melanoma cells.
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Affiliation(s)
- Yu-Wen Yeh
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
- Division of Dermatology, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei 105, Taiwan
| | - Tung-Wei Hsu
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Surgery, Division of General Surgery, Shuang Ho Hospital, Taipei Medical University, Taipei 235, Taiwan
| | - Yen-Hao Su
- Department of Surgery, Division of General Surgery, Shuang Ho Hospital, Taipei Medical University, Taipei 235, Taiwan
- Department of General Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Chih-Hsin Wang
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Po-Hsiang Liao
- Department of Surgery, Division of General Surgery, Shuang Ho Hospital, Taipei Medical University, Taipei 235, Taiwan
| | - Ching-Feng Chiu
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
- Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei 110, Taiwan
| | - Po-Chen Tseng
- Department of Ophthalmology, Taipei City Hospital, Renai Branch, Taipei 106, Taiwan
- Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Tim-Mo Chen
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Woan-Ruoh Lee
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Yuan-Sheng Tzeng
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Department of Surgery, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, Taiwan
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He Q, Li W, Shi Y, Yu Y, Geng W, Sun Z, Wang RK. SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers. BIOMEDICAL OPTICS EXPRESS 2023; 14:4929-4946. [PMID: 37791269 PMCID: PMC10545193 DOI: 10.1364/boe.497602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 10/05/2023]
Abstract
We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.
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Affiliation(s)
- Qinghua He
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Wanyu Li
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Yaping Shi
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Yi Yu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Wenqian Geng
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Zhiyuan Sun
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Ruikang K Wang
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
- Department of Ophthalmology, University of Washington, Seattle, Washington 98109, USA
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Liao WC, Mukundan A, Sadiaza C, Tsao YM, Huang CW, Wang HC. Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4383-4405. [PMID: 37799695 PMCID: PMC10549751 DOI: 10.1364/boe.492635] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 10/07/2023]
Abstract
One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.
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Affiliation(s)
- Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Cleorita Sadiaza
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila, 1015, Philippines
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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Lin YT, Finlayson GD. A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:4155. [PMID: 37112497 PMCID: PMC10142338 DOI: 10.3390/s23084155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 06/19/2023]
Abstract
Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it is argued that the same RGB can map to different spectra depending on the context with respect to which it is seen and, more generally, that accounting for spatial context leads to improved SR. However, as it stands, DNN performance is only slightly better than the much simpler pixel-based methods where spatial context is not used. In this paper, we present a new pixel-based algorithm called A++ (an extension of the A+ sparse coding algorithm). In A+, RGBs are clustered, and within each cluster, a designated linear SR map is trained to recover spectra. In A++, we cluster the spectra instead in an attempt to ensure neighboring spectra (i.e., spectra in the same cluster) are recovered by the same SR map. A polynomial regression framework is developed to estimate the spectral neighborhoods given only the RGB values in testing, which in turn determines which mapping should be used to map each testing RGB to its reconstructed spectrum. Compared to the leading DNNs, not only does A++ deliver the best results, it is parameterized by orders of magnitude fewer parameters and has a significantly faster implementation. Moreover, in contradistinction to some DNN methods, A++ uses pixel-based processing, which is robust to image manipulations that alter the spatial context (e.g., blurring and rotations). Our demonstration on the scene relighting application also shows that, while SR methods, in general, provide more accurate relighting results compared to the traditional diagonal matrix correction, A++ provides superior color accuracy and robustness compared to the top DNN methods.
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Golovynskyi S, Golovynska I, Roganova O, Golovynskyi A, Qu J, Ohulchanskyy TY. Hyperspectral imaging of lipids in biological tissues using near-infrared and shortwave infrared transmission mode: A pilot study. JOURNAL OF BIOPHOTONICS 2023:e202300018. [PMID: 37021842 DOI: 10.1002/jbio.202300018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/21/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the near-infrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter. The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins, and water. The enhanced protocol demonstrates the ability to visualize small adipose inclusions of 1-10 μm size.
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Affiliation(s)
- Sergii Golovynskyi
- Shenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, People's Republic of China
| | - Iuliia Golovynska
- Shenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, People's Republic of China
| | - Olena Roganova
- V.M. Glushkov Institute of Cybernetics, National Academy of Sciences, Kyiv, Ukraine
| | - Andrii Golovynskyi
- V.M. Glushkov Institute of Cybernetics, National Academy of Sciences, Kyiv, Ukraine
| | - Junle Qu
- Shenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, People's Republic of China
| | - Tymish Y Ohulchanskyy
- Shenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, People's Republic of China
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Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5. J Clin Med 2023; 12:jcm12031134. [PMID: 36769781 PMCID: PMC9918106 DOI: 10.3390/jcm12031134] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.
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Knoedler S, Hoch CC, Huelsboemer L, Knoedler L, Stögner VA, Pomahac B, Kauke-Navarro M, Colen D. Postoperative free flap monitoring in reconstructive surgery-man or machine? Front Surg 2023; 10:1130566. [PMID: 36911625 PMCID: PMC9992807 DOI: 10.3389/fsurg.2023.1130566] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
Free tissue transfer is widely used for the reconstruction of complex tissue defects. The survival of free flaps depends on the patency and integrity of the microvascular anastomosis. Accordingly, the early detection of vascular comprise and prompt intervention are indispensable to increase flap survival rates. Such monitoring strategies are commonly integrated into the perioperative algorithm, with clinical examination still being considered the gold standard for routine free flap monitoring. Despite its widespread acceptance as state of the art, the clinical examination also has its pitfalls, such as the limited applicability in buried flaps and the risk of poor interrater agreement due to inconsistent flap (failure) appearances. To compensate for these shortcomings, a plethora of alternative monitoring tools have been proposed in recent years, each of them with inherent strengths and limitations. Given the ongoing demographic change, the number of older patients requiring free flap reconstruction, e.g., after cancer resection, is rising. Yet, age-related morphologic changes may complicate the free flap evaluation in elderly patients and delay the prompt detection of clinical signs of flap compromise. In this review, we provide an overview of currently available and employed methods for free flap monitoring, with a special focus on elderly patients and how senescence may impact standard free flap monitoring strategies.
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Affiliation(s)
- Samuel Knoedler
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
- Correspondence: Samuel Knoedler Martin Kauke-Navarro
| | - Cosima C. Hoch
- Department of Otolaryngology, Head and Neck Surgery, Rechts der Isar Hospital, Technical University Munich, Munich, Germany
| | - Lioba Huelsboemer
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
| | - Leonard Knoedler
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
| | - Viola A. Stögner
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
| | - Bohdan Pomahac
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
| | - Martin Kauke-Navarro
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
- Correspondence: Samuel Knoedler Martin Kauke-Navarro
| | - David Colen
- Department of Surgery, Division of Plastic Surgery, Yale School of Medicine, Yale New Haven Hospital,New Haven, CT, United States
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12
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Can we Restore Balance to Geometric Morphometrics? A Theoretical Evaluation of how Sample Imbalance Conditions Ordination and Classification. Evol Biol 2022. [DOI: 10.1007/s11692-022-09590-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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13
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Li Y, Shi X, Yang L, Pu C, Tan Q, Yang Z, Huang H. MC-GAT: multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from hyperspectral pathological images. BIOMEDICAL OPTICS EXPRESS 2022; 13:5794-5812. [PMID: 36733731 PMCID: PMC9872896 DOI: 10.1364/boe.472106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/24/2022] [Accepted: 10/01/2022] [Indexed: 06/18/2023]
Abstract
Accurate histopathological analysis is the core step of early diagnosis of cholangiocarcinoma (CCA). Compared with color pathological images, hyperspectral pathological images have advantages for providing rich band information. Existing algorithms of HSI classification are dominated by convolutional neural network (CNN), which has the deficiency of distorting spectral sequence information of HSI data. Although vision transformer (ViT) alleviates this problem to a certain extent, the expressive power of transformer encoder will gradually decrease with increasing number of layers, which still degrades the classification performance. In addition, labeled HSI samples are limited in practical applications, which restricts the performance of methods. To address these issues, this paper proposed a multi-layer collaborative generative adversarial transformer termed MC-GAT for CCA classification from hyperspectral pathological images. MC-GAT consists of two pure transformer-based neural networks including a generator and a discriminator. The generator learns the implicit probability of real samples and transforms noise sequences into band sequences, which produces fake samples. These fake samples and corresponding real samples are mixed together as input to confuse the discriminator, which increases model generalization. In discriminator, a multi-layer collaborative transformer encoder is designed to integrate output features from different layers into collaborative features, which adaptively mines progressive relations from shallow to deep encoders and enhances the discriminating power of the discriminator. Experimental results on the Multidimensional Choledoch Datasets demonstrate that the proposed MC-GAT can achieve better classification results than many state-of-the-art methods. This confirms the potentiality of the proposed method in aiding pathologists in CCA histopathological analysis from hyperspectral imagery.
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Affiliation(s)
- Yuan Li
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
| | - Xu Shi
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
| | - Liping Yang
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
| | - Chunyu Pu
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
| | - Qijuan Tan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030, China
| | - Zhengchun Yang
- Department of ultrasound, Chongqing Health Center for Women and Children, Chongqing 401147, China
- Department of ultrasound, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China
| | - Hong Huang
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
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14
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Wen YC, Wen S, Hsu L, Chi S. Spectral Reflectance Recovery from the Quadcolor Camera Signals Using the Interpolation and Weighted Principal Component Analysis Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166288. [PMID: 36016049 PMCID: PMC9416231 DOI: 10.3390/s22166288] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/14/2022] [Accepted: 08/19/2022] [Indexed: 05/25/2023]
Abstract
The recovery of surface spectral reflectance using the quadcolor camera was numerically studied. Assume that the RGB channels of the quadcolor camera are the same as the Nikon D5100 tricolor camera. The spectral sensitivity of the fourth signal channel was tailored using a color filter. Munsell color chips were used as reflective surfaces. When the interpolation method or the weighted principal component analysis (wPCA) method is used to reconstruct spectra, using the quadcolor camera can effectively reduce the mean spectral error of the test samples compared to using the tricolor camera. Except for computation time, the interpolation method outperforms the wPCA method in spectrum reconstruction. A long-pass optical filter can be applied to the fourth channel for reducing the mean spectral error. A short-pass optical filter can be applied to the fourth channel for reducing the mean color difference, but the mean spectral error will be larger. Due to the small color difference, the quadcolor camera using an optimized short-pass filter may be suitable as an imaging colorimeter. It was found that an empirical design rule to keep the color difference small is to reduce the error in fitting the color-matching functions using the camera spectral sensitivity functions.
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Affiliation(s)
- Yu-Che Wen
- Department of Electrophysics, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu 30010, Taiwan
| | - Senfar Wen
- Department of Electrical Engineering, Yuan Ze University, No. 135 Yuan-Tung Road, Taoyuan 320, Taiwan
| | - Long Hsu
- Department of Electrophysics, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu 30010, Taiwan
| | - Sien Chi
- Department of Photonics, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu 30010, Taiwan
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15
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A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images. J Clin Med 2022; 11:jcm11154392. [PMID: 35956008 PMCID: PMC9369039 DOI: 10.3390/jcm11154392] [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: 07/03/2022] [Revised: 07/23/2022] [Accepted: 07/27/2022] [Indexed: 12/07/2022] Open
Abstract
The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions’ shape should be integrated as a fundamental part of future detection techniques in clinical screening.
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16
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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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17
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Raita-Hakola AM, Annala L, Lindholm V, Trops R, Näsilä A, Saari H, Ranki A, Pölönen I. FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications. SENSORS 2022; 22:s22093420. [PMID: 35591109 PMCID: PMC9103796 DOI: 10.3390/s22093420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/13/2022] [Accepted: 04/23/2022] [Indexed: 01/27/2023]
Abstract
Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.
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Affiliation(s)
- Anna-Maria Raita-Hakola
- Faculty of Information Technology, University of Jyväskylä, 40100 Jyväskylä, Finland; (L.A.); (I.P.)
- Correspondence:
| | - Leevi Annala
- Faculty of Information Technology, University of Jyväskylä, 40100 Jyväskylä, Finland; (L.A.); (I.P.)
| | - Vivian Lindholm
- Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland; (V.L.); (A.R.)
| | - Roberts Trops
- VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland; (R.T.); (A.N.); (H.S.)
| | - Antti Näsilä
- VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland; (R.T.); (A.N.); (H.S.)
| | - Heikki Saari
- VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland; (R.T.); (A.N.); (H.S.)
| | - Annamari Ranki
- Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland; (V.L.); (A.R.)
| | - Ilkka Pölönen
- Faculty of Information Technology, University of Jyväskylä, 40100 Jyväskylä, Finland; (L.A.); (I.P.)
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18
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Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures. J Clin Med 2022; 11:jcm11092315. [PMID: 35566440 PMCID: PMC9102335 DOI: 10.3390/jcm11092315] [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: 03/24/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 11/17/2022] Open
Abstract
Non-melanoma skin cancer, and basal cell carcinoma in particular, is one of the most common types of cancer. Although this type of malignancy has lower metastatic rates than other types of skin cancer, its locally destructive nature and the advantages of its timely treatment make early detection vital. The combination of multispectral imaging and artificial intelligence has arisen as a powerful tool for the detection and classification of skin cancer in a non-invasive manner. The present study uses hyperspectral images to discern between healthy and basal cell carcinoma hyperspectral signatures. Upon the combined use of convolutional neural networks, with a final support vector machine activation layer, the present study reaches up to 90% accuracy, with an area under the receiver operating characteristic curve being calculated at 0.9 as well. While the results are promising, future research should build upon a dataset with a larger number of patients.
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19
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Lin YT, Finlayson GD. On the Optimization of Regression-Based Spectral Reconstruction. SENSORS (BASEL, SWITZERLAND) 2021; 21:5586. [PMID: 34451030 PMCID: PMC8402277 DOI: 10.3390/s21165586] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/14/2021] [Accepted: 08/15/2021] [Indexed: 11/16/2022]
Abstract
Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)-an ℓ1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is-because in SR the linear systems are large and ill-posed-that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training-we formulate both ℓ2 and ℓ1 relative error variants where the latter is MRAE-and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy.
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Affiliation(s)
- Yi-Tun Lin
- School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK;
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20
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Courtenay LA, Herranz-Rodrigo D, Yravedra J, Vázquez-Rodríguez JM, Huguet R, Barja I, Maté-González MÁ, Fernández MF, Muñoz-Nieto ÁL, González-Aguilera D. 3D Insights into the Effects of Captivity on Wolf Mastication and Their Tooth Marks; Implications in Ecological Studies of Both the Past and Present. Animals (Basel) 2021; 11:2323. [PMID: 34438780 PMCID: PMC8388415 DOI: 10.3390/ani11082323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 11/25/2022] Open
Abstract
Human populations have been known to develop complex relationships with large carnivore species throughout time, with evidence of both competition and collaboration to obtain resources throughout the Pleistocene. From this perspective, many archaeological and palaeontological sites present evidence of carnivore modifications to bone. In response to this, specialists in the study of microscopic bone surface modifications have resorted to the use of 3D modeling and data science techniques for the inspection of these elements, reaching novel limits for the discerning of carnivore agencies. The present research analyzes the tooth mark variability produced by multiple Iberian wolf individuals, with the aim of studying how captivity may affect the nature of tooth marks left on bone. In addition to this, four different populations of both wild and captive Iberian wolves are also compared for a more in-depth comparison of intra-species variability. This research statistically shows that large canid tooth pits are the least affected by captivity, while tooth scores appear more superficial when produced by captive wolves. The superficial nature of captive wolf tooth scores is additionally seen to correlate with other metric features, thus influencing overall mark morphologies. In light of this, the present study opens a new dialogue on the reasons behind this, advising caution when using tooth scores for carnivore identification and contemplating how elements such as stress may be affecting the wolves under study.
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Affiliation(s)
- Lloyd A. Courtenay
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain; (M.Á.M.-G.); (Á.-L.M.-N.); (D.G.-A.)
| | - Darío Herranz-Rodrigo
- Department of Prehistory, Complutense University, Prof. Aranguren s/n, 28040 Madrid, Spain; (D.H.-R.); (J.Y.)
- C. A. I. Archaeometry and Archaeological Analysis, Complutense University, Professor Aranguren 2/n, 28040 Madrid, Spain
| | - José Yravedra
- Department of Prehistory, Complutense University, Prof. Aranguren s/n, 28040 Madrid, Spain; (D.H.-R.); (J.Y.)
- C. A. I. Archaeometry and Archaeological Analysis, Complutense University, Professor Aranguren 2/n, 28040 Madrid, Spain
| | - José Mª Vázquez-Rodríguez
- Department of Prehistory and Archaeology, Humanities Faculty, UNED University, C/Senda del Rey, 7, 28040 Madrid, Spain;
| | - Rosa Huguet
- Institut Català de Paleoecologia Humana I Evolució Social (IPHES), Zona Educacional 4, Campus Sescelades URV (Edifici W3), 43700 Tarragona, Spain;
- Department d’Historia i Historia de l’Art, Universitat Rovira i Virgili (URV), Avinguda de Catalunya 35, 43002 Tarragona, Spain
- Unit Associated to CSIC, Departamento de Paleobiologia, Museo de Ciencias Naturales, Calle José Gutiérrez Abascal, s/n, 28006 Madrid, Spain
| | - Isabel Barja
- Zoology Unit, Department of Biology, Autónoma University of Madrid, C/Darwin 2, Campus Universitario de Cantoblanco, 28049 Madrid, Spain;
- Center of Investigation in Biodiversity and Global Change (CIBC-UAM), Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Miguel Ángel Maté-González
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain; (M.Á.M.-G.); (Á.-L.M.-N.); (D.G.-A.)
- Department of Topographic and Cartography Engineering, Higher Technical School of Engineers in Topography, Geodesy and Cartography, Universidad Politécnica de Madrid, Mercator 2, 28031 Madrid, Spain
| | - Maximiliano Fernández Fernández
- Gran Duque de Alba Institution, Dibutación Provincial de Ávila, Paseo Dos de Mayo, 8, 05001 Ávila, Spain;
- Department of Sciences of Communication and Sociology, Faculty of Communication Sciences, University Rey Juan Carlos, Camino del Molino, s/n, 28943 Madrid, Spain
| | - Ángel-Luis Muñoz-Nieto
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain; (M.Á.M.-G.); (Á.-L.M.-N.); (D.G.-A.)
| | - Diego González-Aguilera
- Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain; (M.Á.M.-G.); (Á.-L.M.-N.); (D.G.-A.)
- Gran Duque de Alba Institution, Dibutación Provincial de Ávila, Paseo Dos de Mayo, 8, 05001 Ávila, Spain;
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