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Majumdar A, Lad J, Tumanova K, Serra S, Quereshy F, Khorasani M, Vitkin A. Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:052915. [PMID: 38077502 PMCID: PMC10704263 DOI: 10.1117/1.jbo.29.5.052915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023]
Abstract
Significance Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption. Aim The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR). Approach A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not. Results Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort. Conclusions ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.
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Affiliation(s)
- Anamitra Majumdar
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Jigar Lad
- McMaster University, Department of Physics and Astronomy, Hamilton, Ontario, Canada
| | - Kseniia Tumanova
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Stefano Serra
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
| | - Fayez Quereshy
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
| | - Mohammadali Khorasani
- University of British Columbia, Department of Surgery, Victoria, British Columbia, Canada
| | - Alex Vitkin
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
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Wójcik W, Hu Z, Ushenko Y, Smolarz A, Soltys I, Dubolazov O, Ushenko O, Litvinenko O, Mikirin I, Gordey I, Pavlyukovich O, Pavlov S, Pavlyukovich N, Amirgaliyeva S, Kalizhanova A, Aitkulov Z. Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films. SENSORS (BASEL, SWITZERLAND) 2024; 24:1589. [PMID: 38475128 DOI: 10.3390/s24051589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/13/2023] [Accepted: 12/22/2023] [Indexed: 03/14/2024]
Abstract
Our work uses a polarization matrix formalism to analyze and algorithmically represent optical anisotropy by open dehydration of blood plasma films. Analytical relations for Jones matrix reconstruction of optical birefringence maps of protein crystal networks of dehydrated biofluid films are found. A technique for 3D step-by-step measurement of the distributions of the elements of the Jones matrix or Jones matrix images (JMI) of the optically birefringent structure of blood plasma films (BPF) has been created. Correlation between JMI maps and corresponding birefringence images of dehydrated BPF and saliva films (SF) obtained from donors and prostate cancer patients was determined. Within the framework of statistical analysis of layer-by-layer optical birefringence maps, the parameters most sensitive to pathological changes in the structure of dehydrated films were found to be the central statistical moments of the 1st to 4th orders. We physically substantiated and experimentally determined the sensitivity of the method of 3D polarization scanning technique of BPF and SF preparations in the diagnosis of endometriosis of uterine tissue.
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Affiliation(s)
- Waldemar Wójcik
- Department of Electronics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland
| | - Zhengbing Hu
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Yuriy Ushenko
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
| | - Andrzej Smolarz
- Department of Electronics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland
| | - Iryna Soltys
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
| | - Oleksander Dubolazov
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
| | - Oleksander Ushenko
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
- Photoelectric Information Center, Research Institute of Zhejiang University, Taizhou 310058, China
| | - Olexandra Litvinenko
- Department of Forensic Medicine and Medical Jurisprudence, Bukovinian State Medical University, 58000 Chernivtsi, Ukraine
| | - Ivan Mikirin
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
| | - Ivan Gordey
- Computer Science Department, Yurii Fedkovich Chernivtsi National University, 58012 Chernivtsi, Ukraine
| | - Oleksandr Pavlyukovich
- Department of Forensic Medicine and Medical Jurisprudence, Bukovinian State Medical University, 58000 Chernivtsi, Ukraine
| | - Sergii Pavlov
- Laboratory of Biomedical Optics, Department of Biomedical Engineering and Optic-Electronic Systems, Faculty for Infocommunications, Radioelectronics and Nanosystems, Vinnytsia National Technical University, 21000 Vinnytsia, Ukraine
| | - Natalia Pavlyukovich
- Department of Forensic Medicine and Medical Jurisprudence, Bukovinian State Medical University, 58000 Chernivtsi, Ukraine
| | | | - Aliya Kalizhanova
- Institute of Information and Computational Technologies CS MES RK, Almaty 050010, Kazakhstan
- Department of IT Engineering, Institute of Automation and Information Technology, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan
| | - Zhalau Aitkulov
- Institute of Information and Computational Technologies CS MES RK, Almaty 050010, Kazakhstan
- Department of Information Technologies and Library Affairs, Institute of Physics, Mathematics and Computing, Kazakh National Women's Teacher Training University, Almaty 050000, Kazakhstan
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Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [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: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
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Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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Wei S, Si L, Huang T, Du S, Yao Y, Dong Y, Ma H. Deep-learning-based cross-modality translation from Stokes image to bright-field contrast. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:102911. [PMID: 37867633 PMCID: PMC10587695 DOI: 10.1117/1.jbo.28.10.102911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/25/2023] [Accepted: 09/25/2023] [Indexed: 10/24/2023]
Abstract
Significance Mueller matrix (MM) microscopy has proven to be a powerful tool for probing microstructural characteristics of biological samples down to subwavelength scale. However, in clinical practice, doctors usually rely on bright-field microscopy images of stained tissue slides to identify characteristic features of specific diseases and make accurate diagnosis. Cross-modality translation based on polarization imaging helps to improve the efficiency and stability in analyzing sample properties from different modalities for pathologists. Aim In this work, we propose a computational image translation technique based on deep learning to enable bright-field microscopy contrast using snapshot Stokes images of stained pathological tissue slides. Taking Stokes images as input instead of MM images allows the translated bright-field images to be unaffected by variations of light source and samples. Approach We adopted CycleGAN as the translation model to avoid requirements on co-registered image pairs in the training. This method can generate images that are equivalent to the bright-field images with different staining styles on the same region. Results Pathological slices of liver and breast tissues with hematoxylin and eosin staining and lung tissues with two types of immunohistochemistry staining, i.e., thyroid transcription factor-1 and Ki-67, were used to demonstrate the effectiveness of our method. The output results were evaluated by four image quality assessment methods. Conclusions By comparing the cross-modality translation performance with MM images, we found that the Stokes images, with the advantages of faster acquisition and independence from light intensity and image registration, can be well translated to bright-field images.
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Affiliation(s)
- Shilong Wei
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
| | - Lu Si
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
| | - Tongyu Huang
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
- Tsinghua University, Department of Biomedical Engineering, Beijing, China
| | - Shan Du
- University of Chinese Academy of Sciences, Shenzhen Hospital, Department of Pathology, Shenzhen, China
| | - Yue Yao
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
| | - Yang Dong
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
| | - Hui Ma
- Tsinghua University, Shenzhen International Graduate School, Shenzhen, China
- Tsinghua University, Department of Biomedical Engineering, Beijing, China
- Tsinghua University, Department of Physics, Beijing, China
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5
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Wang N, Zhang C, Wei X, Yan T, Zhou W, Zhang J, Kang H, Yuan Z, Chen X. Harnessing the power of optical microscopy for visualization and analysis of histopathological images. BIOMEDICAL OPTICS EXPRESS 2023; 14:5451-5465. [PMID: 37854561 PMCID: PMC10581782 DOI: 10.1364/boe.501893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/29/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023]
Abstract
Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.
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Affiliation(s)
- Nan Wang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Chang Zhang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
| | - Xinyu Wei
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
| | - Tianyu Yan
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Wangting Zhou
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Jiaojiao Zhang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Huan Kang
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Macau, 999078, China
| | - Xueli Chen
- Center for Biomedical-photonics and Molecular Imaging, Xi’an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710126, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi’an, Shaanxi 710126, China
- Inovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong 510555, China
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6
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Yin P, Zhou Z, Liu J, Jiang N, Zhang J, Liu S, Wang F, Wang L. A generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split. Phys Med Biol 2023; 68:175039. [PMID: 37536319 DOI: 10.1088/1361-6560/aced34] [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: 03/02/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023]
Abstract
Objective.This study aims to propose a generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split.Approach.We present a neural network framework for cancer diagnosis and prognosis prediction in pathological images. To enhance the network's depth and width, we employ a hierarchical split block (HS-Block) to create an AI-aided diagnosis system suitable for semi-supervised clinical settings with limited labeled samples and cross-domain tasks. By incorporating a lightweight convolution unit based on the HS-Block, we improve the feature information extraction capabilities of a regular network (RegNet). Additionally, we integrate a Convolutional Block Attention Module into the first and last convolutions to optimize the extraction of global features and local details. To address limited sample labels, we employ a dual-transfer learning (DTL) mechanism named DTL-HS-Regnet, enabling semi-supervised learning in clinical settings.Main results.Our proposed DTL-HS-Regnet model outperforms other advanced deep-learning models in three different types of cancer diagnosis tasks. It demonstrates superior feature extraction ability, achieving an average sensitivity, specificity, accuracy, and F1 score of 0.9987, 1.0000, 1.0000 and 0.9992, respectively. Furthermore, we evaluate the model's capability to directly extract prognosis prediction information from pathological images by constructing patient cohorts. The results show that the correlation between DTL-HS-Regnet predictions and the presence of cancer-associated fibroblasts is comparable to that of pathologists.Significance.Our proposed AI method offers a generalized approach for cancer diagnosis and prognosis prediction in pathology. The outstanding performance of the DTL-HS-Regnet model demonstrates its potential for improving current practices in image digital pathology, expanding the boundaries of cancer treatment in two critical areas.
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Affiliation(s)
- Pengzhi Yin
- School of automation, Central South University, 410083, People's Republic of China
| | - Zehao Zhou
- School of Software, Xinjiang University, 830001, People's Republic of China
| | - Jingze Liu
- School of Software, Xinjiang University, 830001, People's Republic of China
| | - Nan Jiang
- XiangYa School of Medicine, Central South University, 410083, People's Republic of China
| | - Junchao Zhang
- School of automation, Central South University, 410083, People's Republic of China
| | - Shiyu Liu
- XiangYa School of Medicine, Central South University, 410083, People's Republic of China
| | - Feiyang Wang
- XiangYa School of Medicine, Central South University, 410083, People's Republic of China
| | - Li Wang
- College of Computer Science and Technology, Tsinghua University, 100084, People's Republic of China
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Du J, Tao C, Xue S, Zhang Z. Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features. Diagnostics (Basel) 2023; 13:2002. [PMID: 37370897 DOI: 10.3390/diagnostics13122002] [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: 05/07/2023] [Revised: 05/23/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410-900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.
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Affiliation(s)
- Jian Du
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Chenglong Tao
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Shuang Xue
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
| | - Zhoufeng Zhang
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Xi'an Key Laboratory for Biomedical Spectroscopy, Xi'an 710119, China
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Huang T, Yao Y, Pei H, Hu Z, Zhang F, Wang J, Yu G, Huang C, Liu H, Tao L, Ma H. Mueller matrix imaging of pathological slides with plastic coverslips. OPTICS EXPRESS 2023; 31:15682-15696. [PMID: 37157663 DOI: 10.1364/oe.487875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Mueller matrix microscopy is capable of polarization characterization of pathological samples and polarization imaging based digital pathology. In recent years, hospitals are replacing glass coverslips with plastic coverslips for automatic preparations of dry and clean pathological slides with less slide-sticking and air bubbles. However, plastic coverslips are usually birefringent and introduce polarization artifacts in Mueller matrix imaging. In this study, a spatial frequency based calibration method (SFCM) is used to remove such polarization artifacts. The polarization information of the plastic coverslips and the pathological tissues are separated by the spatial frequency analysis, then the Mueller matrix images of pathological tissues are restored by matrix inversions. By cutting two adjacent lung cancer tissue slides, we prepare paired samples of very similar pathological structures but one with a glass coverslip and the other with a plastic coverslip. Comparisons between Mueller matrix images of the paired samples show that SFCM can effectively remove the artifacts due to plastic coverslip.
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Chen Y, Dong Y, Si L, Yang W, Du S, Tian X, Li C, Liao Q, Ma H. Dual Polarization Modality Fusion Network for Assisting Pathological Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:304-316. [PMID: 36155433 DOI: 10.1109/tmi.2022.3210113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Polarization imaging is sensitive to sub-wavelength microstructures of various cancer tissues, providing abundant optical characteristics and microstructure information of complex pathological specimens. However, how to reasonably utilize polarization information to strengthen pathological diagnosis ability remains a challenging issue. In order to take full advantage of pathological image information and polarization features of samples, we propose a dual polarization modality fusion network (DPMFNet), which consists of a multi-stream CNN structure and a switched attention fusion module for complementarily aggregating the features from different modality images. Our proposed switched attention mechanism could obtain the joint feature embeddings by switching the attention map of different modality images to improve their semantic relatedness. By including a dual-polarization contrastive training scheme, our method can synthesize and align the interaction and representation of two polarization features. Experimental evaluations on three cancer datasets show the superiority of our method in assisting pathological diagnosis, especially in small datasets and low imaging resolution cases. Grad-CAM visualizes the important regions of the pathological images and the polarization images, indicating that the two modalities play different roles and allow us to give insightful corresponding explanations and analysis on cancer diagnosis conducted by the DPMFNet. This technique has potential to facilitate the performance of pathological aided diagnosis and broaden the current digital pathology boundary based on pathological image features.
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Li H, Wu P, Wang Z, Mao J, Alsaadi FE, Zeng N. A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis. Comput Biol Med 2022; 151:106265. [PMID: 36401968 DOI: 10.1016/j.compbiomed.2022.106265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. To build a highly generalized computer-aided diagnosis (CAD) system, an information refinement unit employing depth- and point-wise convolutions is meticulously designed, where a dual-domain attention mechanism is adopted to focus primarily on the important areas. By deploying a residual fusion unit, context information is further integrated to extract highly discriminative features with strong representation ability. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, which has achieved average sensitivity, specificity, precision, accuracy and F1 score of 0.9992, 0.9998, 0.9992, 0.9997 and 0.9992 in a five-class cancer detection task, and in comparison to some other advanced deep learning models, above indicators have been improved by 1.23%, 0.31%, 1.24%, 0.5% and 1.26%, respectively. Moreover, the proposed FLE-CNN provides satisfactory results in three important diagnosis, which further validates that FLE-CNN is a competitive CAD model with high generalization ability.
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Affiliation(s)
- Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Jingfeng Mao
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. NPJ Digit Med 2022; 5:156. [PMID: 36261476 PMCID: PMC9581990 DOI: 10.1038/s41746-022-00699-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022] Open
Abstract
Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.
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12
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Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer. Sci Rep 2022; 12:12652. [PMID: 35879367 PMCID: PMC9314438 DOI: 10.1038/s41598-022-16178-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022] Open
Abstract
Using a novel variant of polarized light microscopy for high-contrast imaging and quantification of unstained histology slides, the current study assesses the prognostic potential of peri-tumoral collagenous stroma architecture in 32 human stage III colorectal cancer (CRC) patient samples. We analyze three distinct polarimetrically-derived images and their associated texture features, explore different unsupervised clustering algorithm models to group the data, and compare the resultant groupings with patient survival. The results demonstrate an appreciable total accuracy of ~ 78% with significant separation (p < 0.05) across all approaches for the binary classification of 5-year patient survival outcomes. Surviving patients preferentially belonged to Cluster 1 irrespective of model approach, suggesting similar stromal microstructural characteristics in this sub-population. The results suggest that polarimetrically-derived stromal biomarkers may possess prognostic value that could improve clinical management/treatment stratification in CRC patients.
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13
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Shao C, Chen B, He H, He C, Shen Y, Zhai H, Ma H. Analyzing the Influence of Imaging Resolution on Polarization Properties of Scattering Media Obtained From Mueller Matrix. Front Chem 2022; 10:936255. [PMID: 35903191 PMCID: PMC9315153 DOI: 10.3389/fchem.2022.936255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/09/2022] [Indexed: 11/23/2022] Open
Abstract
The Mueller matrix contains abundant micro- and even nanostructural information of media. Especially, it can be used as a powerful tool to characterize anisotropic structures quantitatively, such as the particle size, density, and orientation information of fibers in the sample. Compared with unpolarized microscopic imaging techniques, Mueller matrix microscopy can also obtain some essential structural information about the sample from the derived parameters images at low resolution. Here, to analyze the comprehensive effects of imaging resolution on polarization properties obtained from the Mueller matrix, we, first, measure the microscopic Mueller matrices of unstained rat dorsal skin tissue slices rich in collagen fibers using a series of magnifications or numerical aperture (NA) values of objectives. Then, the first-order moments and image texture parameters are quantified and analyzed in conjunction with the polarization parameter images. The results show that the Mueller matrix polar decomposition parameters diattenuation D, linear retardance δ, and depolarization Δ images obtained using low NA objective retain most of the structural information of the sample and can provide fast imaging speed. In addition, the scattering phase function analysis and Monte Carlo simulation based on the cylindrical scatterers reveal that the diattenuation parameter D images with different imaging resolutions are expected to be used to distinguish among the fibrous scatterers in the medium with different particle sizes. This study provides a criterion to decide which structural information can be accurately and rapidly obtained using a transmission Mueller matrix microscope with low NA objectives to assist pathological diagnosis and other applications.
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Affiliation(s)
- Conghui Shao
- Department of Physics, Tsinghua University, Beijing, China
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Binguo Chen
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Honghui He
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Honghui He, ; Chao He,
| | - Chao He
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- *Correspondence: Honghui He, ; Chao He,
| | - Yuanxing Shen
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Haoyu Zhai
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Hui Ma
- Department of Physics, Tsinghua University, Beijing, China
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
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14
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Optimal Configurations of Mueller Polarimeter for Gaussian–Poisson Mixed Noise. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The accuracy of the Mueller polarimeter is usually affected by Gaussian–Poisson mixed noise, and by optimizing the instrument matrices of polarization state generator and polarization state analyzer in the measurement system, the estimation variance caused by Gaussian noise can be suppressed, and the estimation variance caused by Poisson noise can be made independent of the sample. However, the optimization procedure usually targets only the numerical value of the instrument matrix without considering how to configure the measurement system to achieve the optimal instrument matrix. In this paper, we investigate how to make the measurement system optimal for different measurement systems by combining geometric optimization on the Poincaré sphere and finally propose a series of measurement configurations for different applications.
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15
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Yang X, Zhao Q, Huang T, Hu Z, Bu T, He H, Hou A, Li M, Xiao Y, Ma H. Deep learning for denoising in a Mueller matrix microscope. BIOMEDICAL OPTICS EXPRESS 2022; 13:3535-3551. [PMID: 35781954 PMCID: PMC9208591 DOI: 10.1364/boe.457219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/05/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
The Mueller matrix microscope is a powerful tool for characterizing the microstructural features of a complex biological sample. Performance of a Mueller matrix microscope usually relies on two major specifications: measurement accuracy and acquisition time, which may conflict with each other but both contribute to the complexity and expenses of the apparatus. In this paper, we report a learning-based method to improve both specifications of a Mueller matrix microscope using a rotating polarizer and a rotating waveplate polarization state generator. Low noise data from long acquisition time are used as the ground truth. A modified U-Net structured network incorporating channel attention effectively reduces the noise in lower quality Mueller matrix images obtained with much shorter acquisition time. The experimental results show that using high quality Mueller matrix data as ground truth, such a learning-based method can achieve both high measurement accuracy and short acquisition time in polarization imaging.
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Affiliation(s)
- Xiongjie Yang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Contributed equally
| | - Qianhao Zhao
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Contributed equally
| | - Tongyu Huang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Zheng Hu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Tongjun Bu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Honghui He
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Anli Hou
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Gynaecology, University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen 518106, China
| | - Migao Li
- Guangdong Liss Optical Instrument Co., Ltd., Guangzhou 510095, China
| | - Yucheng Xiao
- Beijing Normal University - Hong Kong Baptist University United International College, Zhuhai 519085, China
| | - Hui Ma
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
- Department of Physics, Tsinghua University, Beijing 100084, China
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16
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Wan J, Dong Y, Xue JH, Lin L, Du S, Dong J, Yao Y, Li C, Ma H. Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells. BIOMEDICAL OPTICS EXPRESS 2022; 13:3339-3354. [PMID: 35781945 PMCID: PMC9208602 DOI: 10.1364/boe.456649] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 05/25/2023]
Abstract
We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.
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Affiliation(s)
- Jiachen Wan
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Equal contributors
| | - Yang Dong
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
- Equal contributors
| | - Jing-Hao Xue
- Department of Statistical Science,
University College London, London WC1E 6BT,
UK
| | - Liyan Lin
- Department of Pathology,
Fujian Medical University Cancer Hospital,
Fujian Cancer Hospital, Fuzhou 350014, China
| | - Shan Du
- Department of Pathology,
University of Chinese Academy of Sciences Shenzhen
Hospital, Shenzhen 518106, China
| | - Jia Dong
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
| | - Yue Yao
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
| | - Chao Li
- Department of Pathology,
Fujian Medical University Cancer Hospital,
Fujian Cancer Hospital, Fuzhou 350014, China
| | - Hui Ma
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
- Department of Physics,
Tsinghua University, Beijing 100084,
China
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17
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Yao Y, Zhang F, Wang B, Wan J, Si L, Dong Y, Zhu Y, Liu X, Chen L, Ma H. Polarization imaging-based radiomics approach for the staging of liver fibrosis. BIOMEDICAL OPTICS EXPRESS 2022; 13:1564-1580. [PMID: 35414973 PMCID: PMC8973194 DOI: 10.1364/boe.450294] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/21/2022] [Accepted: 02/04/2022] [Indexed: 05/25/2023]
Abstract
Mueller matrix imaging contains abundant biological microstructure information and has shown promising potential in clinical applications. Compared with the ordinary unpolarized light microscopy that relies on the spatial resolution to reveal detailed histological features, Mueller matrix imaging encodes rich information on the microstructures even at low-resolution and wide-field conditions. Accurate staging of liver fibrosis is essential for the therapeutic diagnosis and prognosis of chronic liver diseases. In the clinic, pathologists commonly use semiquantitative numerical scoring systems to determine the stages of liver fibrosis based on the visualization of stained characteristic morphological changes, which require skilled staining technicians and well-trained pathologists. A polarization imaging based quantitative diagnostic method can help to reduce the time-consuming multiple staining processes and provide quantitative information to facilitate the accurate staging of liver fibrosis. In this study, we report a polarization imaging based radiomics approach to provide quantitative diagnostic features for the staging of liver fibrosis. Comparisons between polarization image features under a 4× objective lens with H&E image features under 4×, 10×, 20×, and 40× objective lenses were performed to highlight the superiority of the high dimensional polarization image features in the characterization of the histological microstructures of liver fibrosis tissues at low-resolution and wide-field conditions.
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Affiliation(s)
- Yue Yao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Fengdi Zhang
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Bin Wang
- Fujian Medical University, Department of Pathology and Institute of Oncology, School of Basic Medical Sciences, Fuzhou 350014, China
- Fujian Medical University, Diagnostic Pathology Center, Fuzhou 350014, China
- Fujian Medical University, Mengchao Hepatobiliary Hospital, Fuzhou 350014, China
| | - Jiachen Wan
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Lu Si
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Yang Dong
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Yuanhuan Zhu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
| | - Xiaolong Liu
- Tsinghua University, Department of Physics, Beijing 100084, China
| | - Lihong Chen
- Fujian Medical University, Department of Pathology and Institute of Oncology, School of Basic Medical Sciences, Fuzhou 350014, China
- Fujian Medical University, Diagnostic Pathology Center, Fuzhou 350014, China
- Fujian Medical University, Mengchao Hepatobiliary Hospital, Fuzhou 350014, China
| | - Hui Ma
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Center for Precision Medicine and Healthcare, Shenzhen 518071, China
- Tsinghua Shenzhen International Graduate School, Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen 518055, China
- Tsinghua University, Department of Physics, Beijing 100084, China
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18
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Hirata K, Sugimori H, Fujima N, Toyonaga T, Kudo K. Artificial intelligence for nuclear medicine in oncology. Ann Nucl Med 2022; 36:123-132. [PMID: 35028877 DOI: 10.1007/s12149-021-01693-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 11/07/2021] [Indexed: 12/12/2022]
Abstract
As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.
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Affiliation(s)
- Kenji Hirata
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan. .,Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan. .,Division of Medical AI Education and Research, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
| | | | - Noriyuki Fujima
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.,Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.,Division of Medical AI Education and Research, Hokkaido University Graduate School of Medicine, Sapporo, Japan.,Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Japan
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