1
|
Zhao J, Shen Y, Liu X, Hou X, Ding X, An Y, Hui H, Tian J, Zhang H. MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction. Med Phys 2024; 51:5492-5509. [PMID: 38700948 DOI: 10.1002/mp.17104] [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: 07/19/2023] [Revised: 03/09/2024] [Accepted: 03/24/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. PURPOSE Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. METHODS The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. RESULTS Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. CONCLUSION Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.
Collapse
Affiliation(s)
- Jing Zhao
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yusong Shen
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xinyi Liu
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xiaoyuan Hou
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China
| | - Xuetong Ding
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yu An
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beihang University, Beijing, China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- School of Computer Science and Engineering, Southeast University, Nanjing, China
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hui Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beihang University, Beijing, China
| |
Collapse
|
2
|
Sezgin E, McKay I. Behavioral health and generative AI: a perspective on future of therapies and patient care. NPJ MENTAL HEALTH RESEARCH 2024; 3:25. [PMID: 38849499 PMCID: PMC11161641 DOI: 10.1038/s44184-024-00067-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/06/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Emre Sezgin
- The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
- The Ohio State University College of Medicine, Columbus, OH, USA.
| | - Ian McKay
- The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Psychiatry and Behavioral Health, Nationwide Children's Hospital, Columbus, OH, USA
| |
Collapse
|
3
|
Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [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: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
Collapse
Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| |
Collapse
|
4
|
Chen R, Zhang W, Song F, Yu H, Cao D, Zheng Y, He M, Shi D. Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening. NPJ Digit Med 2024; 7:34. [PMID: 38347098 PMCID: PMC10861476 DOI: 10.1038/s41746-024-01018-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 01/18/2024] [Indexed: 02/15/2024] Open
Abstract
Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79-0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage.
Collapse
Affiliation(s)
- Ruoyu Chen
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Weiyi Zhang
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Fan Song
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China
| | - Dan Cao
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
| | - Mingguang He
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong SAR, China.
| | - Danli Shi
- Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| |
Collapse
|
5
|
Leng J, Zhu J, Yan Y, Yu X, Liu M, Lou Y, Liu Y, Gao L, Sun Y, He T, Yang Q, Feng C, Wang D, Zhang Y, Xu Q, Xu F. Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model. Int J Neural Syst 2024; 34:2350067. [PMID: 38149912 DOI: 10.1142/s0129065723500673] [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] [Indexed: 12/28/2023]
Abstract
Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.
Collapse
Affiliation(s)
- Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Jianqun Zhu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yihao Yan
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yitai Lou
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yanbing Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Licai Gao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yuan Sun
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Tianzheng He
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Qingbo Yang
- School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Chao Feng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Dezheng Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
| | - Yang Zhang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
| | - Qing Xu
- Shandong Institute of Scientific and Technical Information, Jinan 250101, P. R. China
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| |
Collapse
|
6
|
Kim J, Chin HS. Deep learning-based prediction of the retinal structural alterations after epiretinal membrane surgery. Sci Rep 2023; 13:19275. [PMID: 37935769 PMCID: PMC10630279 DOI: 10.1038/s41598-023-46063-6] [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: 07/19/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
To generate and evaluate synthesized postoperative OCT images of epiretinal membrane (ERM) based on preoperative OCT images using deep learning methodology. This study included a total 500 pairs of preoperative and postoperative optical coherence tomography (OCT) images for training a neural network. 60 preoperative OCT images were used to test the neural networks performance, and the corresponding postoperative OCT images were used to evaluate the synthesized images in terms of structural similarity index measure (SSIM). The SSIM was used to quantify how similar the synthesized postoperative OCT image was to the actual postoperative OCT image. The Pix2Pix GAN model was used to generate synthesized postoperative OCT images. Total 60 synthesized OCT images were generated with training values at 800 epochs. The mean SSIM of synthesized postoperative OCT to the actual postoperative OCT was 0.913. Pix2Pix GAN model has a possibility to generate predictive postoperative OCT images following ERM removal surgery.
Collapse
Affiliation(s)
- Joseph Kim
- Retina Division, Nune Eye Hospital, Seoul, Republic of Korea
| | - Hee Seung Chin
- Department of Ophthalmology, Inha University School of Medicine, Incheon, Republic of Korea.
| |
Collapse
|
7
|
Li B, Zhan C. Distributed Diagnoses Based on Constructing a Private Chain via a Public Network. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1305. [PMID: 37761604 PMCID: PMC10530034 DOI: 10.3390/e25091305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
Secure online consultations can provide convenient medical services to patients who require experts from different regions. Moreover, this process can save time, which is critical in emergency cases, and cut medical costs. However, medical services need a high level of privacy protection that advances the difficulty of a construction method. It is a good idea to construct a virtual private chain through public networks by means of cryptology and identity verification. For this purpose, novel protocols are proposed to finish the package layout, secure transmission, and authorization. By mining the special characteristics of this application, two different kinds of encryption channels were designed to support the proposed protocol to ensure the secure transmission of data. And Hash values and multiple checking were employed in the transmission package to find the incompleteness of data related to network errors or attacks. Besides the secure communication of medical information, the Extended Chinese Remainder Theorem was utilized to finish the approval during a change in committee in emergency situations. Finally, example case was used to verify the effectiveness of the total methods.
Collapse
Affiliation(s)
- Bing Li
- School of Economics, Wuhan University of Technology, Wuhan 430070, China
| | - Choujun Zhan
- School of Computer, South China Normal University, Guangzhou 510631, China
| |
Collapse
|
8
|
Li X, Zhang H, Yang H, Li TQ. CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism. SENSORS (BASEL, SWITZERLAND) 2023; 23:7685. [PMID: 37765747 PMCID: PMC10537966 DOI: 10.3390/s23187685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/20/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023]
Abstract
Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN's average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.
Collapse
Affiliation(s)
- Xia Li
- College of Information Engineering, China Jiliang University, Hangzhou 310018, China
| | - Hui Zhang
- College of Information Engineering, China Jiliang University, Hangzhou 310018, China
| | - Hao Yang
- College of Information Engineering, China Jiliang University, Hangzhou 310018, China
| | - Tie-Qiang Li
- Department of Clinical Science, Intervention, and Technology, Karolinska Institute, 14186 Stockholm, Sweden
- Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, 17176 Stockholm, Sweden
| |
Collapse
|
9
|
Dolezal JM, Wolk R, Hieromnimon HM, Howard FM, Srisuwananukorn A, Karpeyev D, Ramesh S, Kochanny S, Kwon JW, Agni M, Simon RC, Desai C, Kherallah R, Nguyen TD, Schulte JJ, Cole K, Khramtsova G, Garassino MC, Husain AN, Li H, Grossman R, Cipriani NA, Pearson AT. Deep learning generates synthetic cancer histology for explainability and education. NPJ Precis Oncol 2023; 7:49. [PMID: 37248379 PMCID: PMC10227067 DOI: 10.1038/s41698-023-00399-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/12/2023] [Indexed: 05/31/2023] Open
Abstract
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.
Collapse
Affiliation(s)
- James M Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Rachelle Wolk
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Hanna M Hieromnimon
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Frederick M Howard
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | | | | | - Siddhi Ramesh
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Sara Kochanny
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Jung Woo Kwon
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Meghana Agni
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Richard C Simon
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Chandni Desai
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Raghad Kherallah
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Tung D Nguyen
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Jefree J Schulte
- Department of Pathology and Laboratory Medicine, University of Wisconsin at Madison, Madison, WN, USA
| | - Kimberly Cole
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Galina Khramtsova
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Marina Chiara Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
| | - Aliya N Husain
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Huihua Li
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Robert Grossman
- University of Chicago, Center for Translational Data Science, Chicago, IL, USA
| | - Nicole A Cipriani
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA.
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago Medicine, Chicago, IL, USA.
| |
Collapse
|
10
|
Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med 2023; 152:106443. [PMID: 36563539 DOI: 10.1016/j.compbiomed.2022.106443] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.
Collapse
Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Shadi Dorosti
- Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
| | | | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | | | - Sadia Samar Ali
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Zahra Arshadi
- Faculty of Electronics, Telecommunications and Physics Engineering, Polytechnic University, Turin, Italy.
| | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
| |
Collapse
|