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Bai A, Si M, Xue P, Qu Y, Jiang Y. Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2024; 24:13. [PMID: 38191361 PMCID: PMC10775443 DOI: 10.1186/s12911-023-02397-9] [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: 02/01/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time. METHODS Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386. RESULTS Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or > 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia). CONCLUSIONS Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.
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Affiliation(s)
- Anying Bai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yimin Qu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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2
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Jiang S, Ma D, Tan X, Yang M, Jiao Q, Xu L. Bibliometric analysis of the current status and trends on medical hyperspectral imaging. Front Med (Lausanne) 2023; 10:1235955. [PMID: 37795419 PMCID: PMC10545955 DOI: 10.3389/fmed.2023.1235955] [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: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.
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Affiliation(s)
| | | | | | | | | | - Liang Xu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin,China
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3
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Ryu D, Bak T, Ahn D, Kang H, Oh S, Min HS, Lee S, Lee J. Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon 2023; 9:e18297. [PMID: 37576294 PMCID: PMC10412892 DOI: 10.1016/j.heliyon.2023.e18297] [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: 03/04/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.
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Affiliation(s)
- Dongmin Ryu
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Taeyoung Bak
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hayoung Kang
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Sanggeun Oh
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | | | - Sumin Lee
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Jimin Lee
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
- Graduate School of Artificial Intelligence (AIGS), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
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4
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Qin X, Zhang M, Zhou C, Ran T, Pan Y, Deng Y, Xie X, Zhang Y, Gong T, Zhang B, Zhang L, Wang Y, Li Q, Wang D, Gao L, Zou D. A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma. Cancer Med 2023; 12:17005-17017. [PMID: 37455599 PMCID: PMC10501295 DOI: 10.1002/cam4.6335] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 06/12/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
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Affiliation(s)
- Xianzheng Qin
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Minmin Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Chunhua Zhou
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Taojing Ran
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yundi Pan
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yingjiao Deng
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Xingran Xie
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Yao Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Tingting Gong
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Benyan Zhang
- Department of PathologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Ling Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Dong Wang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Lili Gao
- Department of PathologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Duowu Zou
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
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5
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Aloupogianni E, Ishikawa M, Ichimura T, Hamada M, Murakami T, Sasaki A, Nakamura K, Kobayashi N, Obi T. Effects of dimension reduction of hyperspectral images in skin gross pathology. Skin Res Technol 2023; 29:e13270. [PMID: 36823506 PMCID: PMC10155843 DOI: 10.1111/srt.13270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/17/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep-learning models, dimension reduction is a common preprocessing step. The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. MATERIALS AND METHODS An in-house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster-wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. RESULTS The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. CONCLUSION Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.
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Affiliation(s)
- Eleni Aloupogianni
- Department of Information and Communications EngineeringTokyo Institute of TechnologyYokohamaJapan
| | - Masahiro Ishikawa
- Faculty of Health and Medical CareSaitama Medical University Hidaka CampusHidakaJapan
| | - Takaya Ichimura
- Department of PathologyFaculty of MedicineSaitama Medical University Moroyama CampusMoroyamaJapan
| | - Mei Hamada
- Department of PathologyFaculty of MedicineSaitama Medical University Moroyama CampusMoroyamaJapan
| | - Takuo Murakami
- Department of DermatologyFaculty of MedicineSaitama Medical University Moroyama CampusMoroyamaJapan
| | - Atsushi Sasaki
- Department of PathologyFaculty of MedicineSaitama Medical University Moroyama CampusMoroyamaJapan
| | - Koichiro Nakamura
- Department of DermatologyFaculty of MedicineSaitama Medical University Moroyama CampusMoroyamaJapan
| | - Naoki Kobayashi
- Department of Information and Communications EngineeringTokyo Institute of TechnologyYokohamaJapan
| | - Takashi Obi
- Department of Information and Communications EngineeringTokyo Institute of TechnologyYokohamaJapan
- Institute of Innovative Research, Tokyo Institute of TechnologyTokyoJapan
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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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S A, Ganesan K, K BB. A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images. BIOMED ENG-BIOMED TE 2022; 68:175-185. [PMID: 36197949 DOI: 10.1515/bmt-2021-0127] [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: 04/29/2021] [Accepted: 09/13/2022] [Indexed: 11/15/2022]
Abstract
Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database 'erythrocytes IDB'. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.
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Affiliation(s)
- Alagu S
- Department of Electronics Engineering, Madras Institute of Technology, Chennai, India
| | - Kavitha Ganesan
- Department of Electronics Engineering, Madras Institute of Technology, Chennai, India
| | - Bhoopathy Bagan K
- Department of Electronics Engineering, Madras Institute of Technology, Chennai, India
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Chen D, Li N, Liu X, Zeng S, Lv X, Chen L, Xiao Y, Hu Q. Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light. BIOMEDICAL OPTICS EXPRESS 2022; 13:4752-4772. [PMID: 36187242 PMCID: PMC9484434 DOI: 10.1364/boe.466162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/16/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.
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Affiliation(s)
- Duan Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Ning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiaohua Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Li Chen
- Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuwei Xiao
- Wuhan Hannan People’s Hospital, Wuhan 430090, China
| | - Qinglei Hu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
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Sengar N, Burget R, Dutta MK. A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:106996. [PMID: 35843076 DOI: 10.1016/j.cmpb.2022.106996] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. METHODS In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. RESULTS The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. CONCLUSIONS A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.
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Affiliation(s)
| | - Radim Burget
- Brno University of Technology, FEEC, Dept. of Telecommunications, 616 00 Brno, Czech Republic
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10
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Panda A, Pachori RB, Kakkar N, Joseph John M, Sinnappah-Kang ND. Screening chronic myeloid leukemia neutrophils using a novel 3-Dimensional Spectral Gradient Mapping algorithm on hyperspectral images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106836. [PMID: 35523026 DOI: 10.1016/j.cmpb.2022.106836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 04/17/2022] [Accepted: 04/23/2022] [Indexed: 06/14/2023]
Abstract
Background and objective Early diagnosis of chronic myeloid leukemia (CML) is important for effective treatment. The high spectral and spatial resolution of hyperspectral cellular or tissue images coupled with image analysis algorithms may provide avenues to detect and diagnose diseases early. Many algorithms have been used to analyze medical hyperspectral image data, each having their own strengths and short-comings. We present a novel 3-Dimensional Spectral Gradient Mapping (3-D SGM) method to analyze hyperspectral image cubes of CML versus healthy blood smears. Methods In the present study, we analyzed 13 hyperspectral image cubes of CML and healthy neutrophils. The 3-D SGM algorithm was compared to the conventional Windowed Spectral Angle Mapping (Windowed SAM) method. The 3-D SGM exploited the spectral information of the image cube together with the inter-band and inter-pixel data by extracting the 3-D gradient vector from each pixel. The Windowed SAM determined the similarity between the averaged window of a 2×2 training pixel group and the test pixel, in the multidimensional spectral angle. Results The specificity measure of 3-D SGM (97.7%) was superior to Windowed SAM (72.7%) at ruling out the presence of the disease, making it potentially ideal for screening patients. The positive likelihood ratio value of 3-D SGM (16.70) was superior in diagnosing the presence of the disease (i.e., positive test for CML) versus Windowed SAM (2.26). An accuracy value of 84.2% was achieved with 3-D SGM versus only 70.2% for Windowed SAM. Conclusion The new method is efficient and robust for analyzing hyperspectral images of CML versus healthy neutrophils. It has the potential to be developed into an inexpensive, minimally invasive method for screening CML, and could directly facilitate early diagnosis and treatment of the disease.
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Affiliation(s)
- Amrit Panda
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India.
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Naveen Kakkar
- Department of Pathology, Christian Medical College and Hospital, Ludhiana, India
| | - M Joseph John
- Department of Clinical Hematology, Hemato-Oncology and Bone Marrow (Stem Cell) Transplantation, Christian Medical College and Hospital, Ludhiana, India
| | - Neeta Devi Sinnappah-Kang
- Betty Cowan Research and Innovation Centre, Christian Medical College and Hospital, Ludhiana, India.
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11
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Ma L, Little JV, Chen AY, Myers L, Sumer BD, Fei B. Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:046501. [PMID: 35484692 PMCID: PMC9050479 DOI: 10.1117/1.jbo.27.4.046501] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Automatic, fast, and accurate identification of cancer on histologic slides has many applications in oncologic pathology. AIM The purpose of this study is to investigate hyperspectral imaging (HSI) for automatic detection of head and neck cancer nuclei in histologic slides, as well as cancer region identification based on nuclei detection. APPROACH A customized hyperspectral microscopic imaging system was developed and used to scan histologic slides from 20 patients with squamous cell carcinoma (SCC). Hyperspectral images and red, green, and blue (RGB) images of the histologic slides with the same field of view were obtained and registered. A principal component analysis-based nuclei segmentation method was developed to extract nuclei patches from the hyperspectral images and the coregistered RGB images. Spectra-based support vector machine and patch-based convolutional neural networks (CNNs) were implemented for nuclei classification. The CNNs were trained with RGB patches (RGB-CNN) and hyperspectral patches (HSI-CNN) of the segmented nuclei and the utility of the extra spectral information provided by HSI was evaluated. Furthermore, cancer region identification was implemented by image-wise classification based on the percentage of cancerous nuclei detected in each image. RESULTS RGB-CNN, which mainly used the spatial information of nuclei, resulted in a 0.81 validation accuracy and 0.74 testing accuracy. HSI-CNN, which utilized the spatial and spectral features of the nuclei, showed significant improvement in classification performance and achieved 0.89 validation accuracy as well as 0.82 testing accuracy. Furthermore, the image-wise cancer region identification based on nuclei detection could generally improve the cancer detection rate. CONCLUSIONS We demonstrated that the morphological and spectral information contribute to SCC nuclei differentiation and that the spectral information within hyperspectral images could improve classification performance.
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Affiliation(s)
- Ling Ma
- University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States
- Tianjin University, State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin, China
| | - James V. Little
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Amy Y. Chen
- Emory University School of Medicine, Department of Otolaryngology, Atlanta, Georgia, United States
| | - Larry Myers
- The University of Texas Southwestern Medical Center, Department of Otolaryngology, Dallas, Texas, United States
| | - Baran D. Sumer
- The University of Texas Southwestern Medical Center, Department of Otolaryngology, Dallas, Texas, United States
| | - Baowei Fei
- University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States
- The University of Texas Southwestern Medical Center, Advanced Imaging Research Center, Dallas, Texas, United States
- The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, Texas, United States
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12
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Kouzehkanan ZM, Saghari S, Tavakoli S, Rostami P, Abaszadeh M, Mirzadeh F, Satlsar ES, Gheidishahran M, Gorgi F, Mohammadi S, Hosseini R. A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm. Sci Rep 2022; 12:1123. [PMID: 35064165 PMCID: PMC8782871 DOI: 10.1038/s41598-021-04426-x] [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: 05/29/2021] [Accepted: 12/10/2021] [Indexed: 12/16/2022] Open
Abstract
Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.
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Affiliation(s)
- Zahra Mousavi Kouzehkanan
- School of ECE, College of Engineering, University of Tehran, Tehran, Iran.,Nimaad Health Equipment Development Company, Tehran, Iran
| | - Sepehr Saghari
- Nimaad Health Equipment Development Company, Tehran, Iran.,Graduated Bachelor of Laboratory of Sciences, Paramedical Faculty of Guilan, University of Medical of Sciences, Langarud, Gilan, Iran
| | - Sajad Tavakoli
- Nimaad Health Equipment Development Company, Tehran, Iran.,Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Peyman Rostami
- Nimaad Health Equipment Development Company, Tehran, Iran.,School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Farzaneh Mirzadeh
- Nimaad Health Equipment Development Company, Tehran, Iran.,School of Medicine, Tarbiat Modares University, Tehran, Iran
| | - Esmaeil Shahabi Satlsar
- Nimaad Health Equipment Development Company, Tehran, Iran.,Flow Cytometry Department, Takhte Tavous Patobiology Lab, Tehran, Iran
| | - Maryam Gheidishahran
- Department of Hematology and Blood Transfusion, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Gorgi
- Bachelor of Laboratory of Sciences, Faculty of Paramedical, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeed Mohammadi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Reshad Hosseini
- School of ECE, College of Engineering, University of Tehran, Tehran, Iran.
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13
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An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210662] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.
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14
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Classification of chronic myeloid leukemia neutrophils by hyperspectral imaging using Euclidean and Mahalanobis distances. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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15
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Sun L, Zhou M, Li Q, Hu M, Wen Y, Zhang J, Lu Y, Chu J. Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks. Methods 2021; 202:22-30. [PMID: 33838272 DOI: 10.1016/j.ymeth.2021.04.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/29/2021] [Accepted: 04/03/2021] [Indexed: 01/02/2023] Open
Abstract
This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method. The first benchmark based on the microscopic hyperspectral pathological images is set up. Particularly, 880 scenes of multidimensional hyperspectral Cholangiocarcinoma images are collected and manually labeled each pixel as either tumor or non-tumor for supervised learning. Moreover, each scene from the slide is given a binary label indicating whether it is from a patient or a normal person. Different from traditional RGB images, the HSI acquires pixels in multiple spectral intervals, which is added as an extension on the channel dimension of 3-channel RGB image. This work aims at fully exploiting the spatial-spectral HSI data through a deep Convolution Neural Network (CNN). The whole scene is first divided into several patches. Then they are fed into CNN for the tumor/non-tumor binary prediction and the tumor area regression. The further diagnosis on the scene is made by random forest based on the features from patch prediction. Experiments show that HSI provides a more accurate result than RGB image. Moreover, a spectral interval convolution and normalization scheme are proposed for further mining the spectral information in HSI, which demonstrates the effectiveness of the spatial-spectral data for CC diagnosis.
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Affiliation(s)
- Li Sun
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Mei Zhou
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China.
| | - Menghan Hu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Ying Wen
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Jian Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Yue Lu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Junhao Chu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
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16
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17
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Rehman AU, Qureshi SA. A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues. Photodiagnosis Photodyn Ther 2020; 33:102165. [PMID: 33383204 DOI: 10.1016/j.pdpdt.2020.102165] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 01/27/2023]
Abstract
Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.
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Affiliation(s)
- Aziz Ul Rehman
- Agri & Biophotonics Division, National Institute of Lasers and Optronics College, PIEAS, 45650, Islamabad, Pakistan; Department of Physics and Astronomy Macquarie University, Sydney, 2109, New South Wales, Australia.
| | - Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, 45650, Pakistan
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18
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Baydilli YY, Atila U, Elen A. Learn from one data set to classify all - A multi-target domain adaptation approach for white blood cell classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105645. [PMID: 32702574 DOI: 10.1016/j.cmpb.2020.105645] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Traditional machine learning methods assume that both training and test data come from the same distribution. In this way, it becomes possible to achieve high successes when modelling on the same domain. Unfortunately, in real-world problems, direct transfer between domains is adversely affected due to differences in the data collection process and the internal dynamics of the data. In order to cope with such drawbacks, researchers use a method called "domain adaptation", which enables the successful transfer of information learned in one domain to other domains. In this study, a model that can be used in the classification of white blood cells (WBC) and is not affected by domain differences was proposed. METHODS Only one data set was used as source domain, and an adaptation process was created that made possible the learned knowledge to be used effectively in other domains (multi-target domain adaptation). While constructing the model, we employed data augmentation, data generation and fine-tuning processes, respectively. RESULTS The proposed model has been able to extract "domain-invariant" features and achieved high success rates in the tests performed on nine different data sets. Multi-target domain adaptation accuracy was measured as %98.09. CONCLUSIONS At the end of the study, it has been observed that the proposed model ignores the domain differences and it can adapt in a successful way to target domains. In this way, it becomes possible to classify unlabeled samples rapidly by using only a few number of labeled ones.
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Affiliation(s)
- Yusuf Yargı Baydilli
- Department of Computer Engineering, Faculty of Engineering, Karabük University, Karabük, Turkey.
| | - Umit Atila
- Department of Computer Engineering, Faculty of Engineering, Karabük University, Karabük, Turkey.
| | - Abdullah Elen
- Department of Computer Technology, TOBB Vocational School of Technical Sciences, Karabük University, Karabük, Turkey.
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19
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Zhu Y, Zhang J, Li M, Zhao L, Ren H, Yan L, Zhao G, Zhu C. Rapid determination of spore germinability of Clostridium perfringens based on microscopic hyperspectral imaging technology and chemometrics. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2019.109896] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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20
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Basnet J, Alsadoon A, Prasad PWC, Aloussi SA, Alsadoon OH. A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL). Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10321-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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21
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Al-Dulaimi K, Banks J, Nugyen K, Al-Sabaawi A, Tomeo-Reyes I, Chandran V. Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques. IEEE Rev Biomed Eng 2020; 14:290-306. [PMID: 32746365 DOI: 10.1109/rbme.2020.3004639] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.
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22
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Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10134448] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.
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23
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Ortega S, Halicek M, Fabelo H, Callico GM, Fei B. Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]. BIOMEDICAL OPTICS EXPRESS 2020; 11:3195-3233. [PMID: 32637250 PMCID: PMC7315999 DOI: 10.1364/boe.386338] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 03/28/2020] [Accepted: 05/08/2020] [Indexed: 05/06/2023]
Abstract
Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.
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Affiliation(s)
- Samuel Ortega
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
- These authors contributed equally to this work
| | - Martin Halicek
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
- Department of Biomedical Engineering, Georgia Inst. of Tech. and Emory University, Atlanta, GA 30322, USA
- These authors contributed equally to this work
| | - Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Gustavo M Callico
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Baowei Fei
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
- University of Texas Southwestern Medical Center, Advanced Imaging Research Center, Dallas, TX 75235, USA
- University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX 75235, USA
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Huang Q, Li W, Zhang B, Li Q, Tao R, Lovell NH. Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN. IEEE J Biomed Health Inform 2020; 24:160-170. [DOI: 10.1109/jbhi.2019.2905623] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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25
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Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Med Biol Eng Comput 2019; 57:1783-1811. [PMID: 31201595 DOI: 10.1007/s11517-019-01984-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 04/22/2019] [Indexed: 01/14/2023]
Abstract
Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of features of the cells is essential in the field of medicine. Acute lymphoblastic leukemia is a form of blood cancer caused due to the abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5 years and adults above 50 years of age. Due to the late diagnosis and cost of the devices used for the determination, the mortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledge from the different methodologies used for extracting features from white blood cells and develop a system that would accurately segment the blood smear image by overcoming the drawbacks of the previous works. The objective mentioned above is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEval and the Ranker Search method are used to achieve the feature selection which in turn helps in improvising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormal blood smear. The extracted feature values of a cancerous cell and a normal cell are also shown. The performance of the model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists. Graphical abstract.
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26
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Johansen TH, Møllersen K, Ortega S, Fabelo H, Garcia A, Callico GM, Godtliebsen F. Recent advances in hyperspectral imaging for melanoma detection. WIRES COMPUTATIONAL STATISTICS 2019. [DOI: 10.1002/wics.1465] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Kajsa Møllersen
- Department of Community Medicine UiT The Arctic University of Norway Tromsø Norway
| | - Samuel Ortega
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Himar Fabelo
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Aday Garcia
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Gustavo M. Callico
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Fred Godtliebsen
- Department of Mathematics and Statistics UiT The Arctic University of Norway Tromsø Norway
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27
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Label-free classification of neurons and glia in neural stem cell cultures using a hyperspectral imaging microscopy combined with machine learning. Sci Rep 2019; 9:633. [PMID: 30679652 PMCID: PMC6345994 DOI: 10.1038/s41598-018-37241-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 12/04/2018] [Indexed: 02/06/2023] Open
Abstract
Due to a growing demand for a viable label-free observation method in the biomedical field, many techniques, such as quantitative phase imaging and Raman spectroscopy, have been studied, and a complementary approach, hyperspectral imaging, has also been introduced. We developed a high-speed hyperspectral imaging microscopy imaging method with commercially available apparatus, employing a liquid crystal tunable bandpass filter combined with a pixel-wise machine learning classification. Next, we evaluated the feasibility of the application of this method for stem cell research utilizing neural stem cells. Employing this microscopy method, with a 562 × 562 μm2 field of view, 2048 × 2048 pixel resolution images containing 63 wavelength pixel-wise spectra could be obtained in 30 seconds. The neural stem cells were differentiated into neurons and astroglia (glia), and a four-class cell classification evaluation (including neuronal cell body, glial cell body, process and extracellular region) was conducted under co-cultured conditions. As a result, an average of 88% of the objects of interest were correctly classified, with an average precision of 94%, and more than 99% of the extracellular pixels were correctly segregated. These results indicated that the proposed hyperspectral imaging microscopy is feasible as a label-free observation method for stem cell research.
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28
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Ortega S, Fabelo H, Camacho R, de la Luz Plaza M, Callicó GM, Sarmiento R. Detecting brain tumor in pathological slides using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2018; 9:818-831. [PMID: 29552415 PMCID: PMC5854081 DOI: 10.1364/boe.9.000818] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 01/10/2018] [Accepted: 01/19/2018] [Indexed: 05/16/2023]
Abstract
Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.
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Affiliation(s)
- Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Rafael Camacho
- Department of Pathological Anatomy, University Hospital Dr. Negrín, Las Palmas de Gran Canaria. Barranco de la Ballena, 35010, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - María de la Luz Plaza
- Department of Pathological Anatomy, University Hospital Dr. Negrín, Las Palmas de Gran Canaria. Barranco de la Ballena, 35010, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Gustavo M. Callicó
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Roberto Sarmiento
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain
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High predictive values of RBC membrane-based diagnostics by biophotonics in an integrated approach for Autism Spectrum Disorders. Sci Rep 2017; 7:9854. [PMID: 28852136 PMCID: PMC5574882 DOI: 10.1038/s41598-017-10361-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 08/09/2017] [Indexed: 12/13/2022] Open
Abstract
Membranes attract attention in medicine, concerning lipidome composition and fatty acid correlation with neurological diseases. Hyperspectral dark field microscopy (HDFM), a biophotonic imaging using reflectance spectra, provides accurate characterization of healthy adult RBC identifying a library of 8 spectral end-members. Here we report hyperspectral RBC imaging in children affected by Autism Spectrum Disorder (ASD) (n = 21) compared to healthy age-matched subjects (n = 20), investigating if statistically significant differences in their HDFM spectra exist, that can comprehensively map a membrane impairment involved in disease. A significant difference concerning one end-member (spectrum 4) was found (P value = 0.0021). A thorough statistical treatment evidenced: i) diagnostic performance by the receiving operators curve (ROC) analysis, with cut-offs and very high predictive values (P value = 0.0008) of spectrum 4 for identifying disease; ii) significant correlations of spectrum 4 with clinical parameters and with the RBC membrane deficit of the omega-3 docosahexaenoic acid (DHA) in ASD patients; iii) by principal component analysis, very high affinity values of spectrum 4 to the factor that combines behavioural parameters and the variable “cc” discriminating cases and controls. These results foresee the use of biophotonic methodologies in ASD diagnostic panels combining with molecular elements for a correct neuronal growth.
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