1
|
Bianco V, Valentino M, Pirone D, Miccio L, Memmolo P, Brancato V, Coppola L, Smaldone G, D’Aiuto M, Mossetti G, Salvatore M, Ferraro P. Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy. Comput Struct Biotechnol J 2024; 24:225-236. [PMID: 38572166 PMCID: PMC10990711 DOI: 10.1016/j.csbj.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.
Collapse
Affiliation(s)
- Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Marika Valentino
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, via Claudio 21, 80125 Napoli, Italy
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | | | - Luigi Coppola
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Napoli 80143, Italy
| | | | | | - Gennaro Mossetti
- Pathological Anatomy Service, Casa di Cura Maria Rosaria, Via Colle San Bartolomeo 50, 80045 Pompei, Napoli, Italy
| | | | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Mürer FK, Tekseth KR, Chattopadhyay B, Olstad K, Akram MN, Breiby DW. Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques - a short review including new research. Biomed Phys Eng Express 2024; 10:045041. [PMID: 38744257 DOI: 10.1088/2057-1976/ad4b1f] [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: 11/28/2023] [Accepted: 05/14/2024] [Indexed: 05/16/2024]
Abstract
Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or 'label-free' imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive.Computational imagingdenotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies andin vivopossibilities conclude the article.
Collapse
Affiliation(s)
- Fredrik K Mürer
- Department of Physics, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
- SINTEF Helgeland AS, Halvor Heyerdahls vei 33, 8626 Mo i Rana, Norway
| | - Kim R Tekseth
- Department of Physics, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
| | - Basab Chattopadhyay
- Department of Physics, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
| | - Kristin Olstad
- Faculty of Veterinary Medicine, Department of Companion Animal Clinical Sciences, Norwegian University of Life Sciences (NMBU), Equine section, PO Box 5003, 1432 Ås, Norway
| | - Muhammad Nadeem Akram
- Department of Microsystems, University of South-Eastern Norway (USN), 3184 Borre, Norway
| | - Dag W Breiby
- Department of Physics, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, 7491 Trondheim, Norway
- Department of Microsystems, University of South-Eastern Norway (USN), 3184 Borre, Norway
| |
Collapse
|
4
|
Xu F, Wu Z, Tan C, Liao Y, Wang Z, Chen K, Pan A. Fourier Ptychographic Microscopy 10 Years on: A Review. Cells 2024; 13:324. [PMID: 38391937 PMCID: PMC10887115 DOI: 10.3390/cells13040324] [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: 12/15/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.
Collapse
Affiliation(s)
- Fannuo Xu
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zipei Wu
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
| | - Chao Tan
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
| | - Yizheng Liao
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhiping Wang
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Keru Chen
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - An Pan
- State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China; (F.X.); (Z.W.); (C.T.); (Y.L.); (Z.W.); (K.C.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|