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Bardozzo F, Fiore P, Valentino M, Bianco V, Memmolo P, Miccio L, Brancato V, Smaldone G, Gambacorta M, Salvatore M, Ferraro P, Tagliaferri R. Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy. Comput Biol Med 2024; 179:108861. [PMID: 39018884 DOI: 10.1016/j.compbiomed.2024.108861] [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: 03/06/2024] [Revised: 05/28/2024] [Accepted: 07/06/2024] [Indexed: 07/19/2024]
Abstract
Achieving microscopy with large space-bandwidth products plays a key role in diagnostic imaging and is widely significant in the overall field of clinical practice. Among quantitative microscopy techniques, Fourier Ptychography (FP) provides a wide field of view and high-resolution images, suitable to the histopathological field, but onerous in computational terms. Artificial intelligence can be a solution in this sense. In particular, this research delves into the application of Generative Adversarial Networks (GAN) for the dual-channel complex FP image enhancement of human kidney samples. The study underscores the GANs' efficacy in promoting biological architectures in FP domain, thereby still guaranteeing high resolution and visibility of detailed microscopic structures. We demonstrate successful GAN-based enhanced reconstruction through two strategies: cross-explainability and expert survey. The cross-explainability is evaluated through the comparison of explanation maps for both real and imaginary components underlining its robustness. This comparison further shows that their interplay is pivotal for accurate reconstruction without hallucinations. Secondly, the enhanced reconstruction accuracy and effectiveness in a clinical workflow are confirmed through a two-step survey conducted with nephrologists.
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Affiliation(s)
- Francesco Bardozzo
- NeuroneLab - Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy; CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy
| | - Pierpaolo Fiore
- NeuroneLab - Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy
| | - Marika Valentino
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy; DIETI, Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", via Claudio 21, Napoli, 80125, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy.
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy
| | | | | | | | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Napoli, 80143, Italy
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy.
| | - Roberto Tagliaferri
- NeuroneLab - Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy; CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy
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Zeng X, Zhu Q, Ahmed A, Hanif M, Hou M, Jie Q, Xi R, Shah SA. Multi-granularity prior networks for uncertainty-informed patient-specific quality assurance. Comput Biol Med 2024; 179:108925. [PMID: 39067284 DOI: 10.1016/j.compbiomed.2024.108925] [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: 11/27/2023] [Revised: 05/28/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024]
Abstract
Deep Learning Automated Patient-Specific Quality Assurance (PSQA) aims to reduce clinical resource requirements. It is vital to ensure the safety and effectiveness of radiation therapy by predicting the dose difference metric (Gamma passing rate) and its distribution. However, current research overlooks uncertainty quantification in model predictions, limiting their trustworthiness in real clinical environments. This paper proposes a Multi-granularity Uncertainty Quantification (MGUQ) framework. A Bayesian framework that quantifies uncertainties at multiple granularities for multi-task PSQA, specifically Gamma Passing Rate (GPR) prediction and Dose Difference Prediction (DDP), integrates visualization-based interactive components. Using Bayesian theory, we derive a comprehensive multi-granularity loss function that comprises granularity-specific loss and coherence loss components. Additionally, we proposed Multi-granularity Prior Networks, a dual-stream network architecture, to infer the distributions of DDP (modeled as t-distributions) and GPR (modeled as Gaussian distributions) under specific statistical assumptions. Comprehensive evaluations are conducted on a dataset from ''Peeking Union Medical College Hospital'', and results show that our proposed method achieves a minimum MAE loss of 0.864 with a 2%/3 mm criterion and realizes the uncertainty visualization of dose difference. Further, it also achieves 100% Clinical Accuracy (CA) with a workload of 67.2%. Experiments demonstrate that the proposed framework can enhance the trustworthiness of deep learning applications in PSQA.
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Affiliation(s)
- Xiaoyang Zeng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China - UESTC, Sichuan, 611731, China.
| | - Qizhen Zhu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Awais Ahmed
- School of Computer Science and Engineering, University of Electronic Science and Technology of China - UESTC, Sichuan, 611731, China.
| | - Muhammad Hanif
- School of Computer Science and Engineering, University of Electronic Science and Technology of China - UESTC, Sichuan, 611731, China.
| | - Mengshu Hou
- School of Computer Science and Engineering, University of Electronic Science and Technology of China - UESTC, Sichuan, 611731, China; School of Big Data and Artificial Intelligence, Chengdu Technological University, Sichuan, 611730, China.
| | - Qiu Jie
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Rui Xi
- School of Computer Science and Engineering, University of Electronic Science and Technology of China - UESTC, Sichuan, 611731, China.
| | - Syed Attique Shah
- School of Computing and Digital Technology, Birmingham City University, STEAMhouse, B4 7RQ, Birmingham, United Kingdom.
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Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
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Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
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Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 2024; 92:103046. [PMID: 38052145 DOI: 10.1016/j.media.2023.103046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Affiliation(s)
- Sanuwani Dayarathna
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
| | | | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom
| | - Munawar Hayat
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Zhaolin Chen
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia
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