1
|
Schulte B, Göb M, Singh AP, Lotz S, Draxinger W, Heimke M, Pieper M, Heinze T, Wedel T, Rahlves M, Huber R, Ellrichmann M. High-resolution rectoscopy using MHz optical coherence tomography: a step towards real time 3D endoscopy. Sci Rep 2024; 14:4672. [PMID: 38409328 PMCID: PMC10897148 DOI: 10.1038/s41598-024-55338-5] [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: 12/06/2023] [Accepted: 02/22/2024] [Indexed: 02/28/2024] Open
Abstract
Colonoscopy and endoscopic ultrasound play pivotal roles in the assessment of rectal diseases, especially rectal cancer and inflammatory bowel diseases. Optical coherence tomography (OCT) offers a superior depth resolution, which is a critical factor for individualizing the therapeutic concept and evaluating the therapy response. We developed two distinct rectoscope prototypes, which were integrated into a 1300 nm MHz-OCT system constructed at our facility. The rapid rotation of the distal scanning probe at 40,000 revolutions per minute facilitates a 667 Hz OCT frame rate, enabling real-time endoscopic imaging of large areas. The performance of these OCT-rectoscopes was assessed in an ex vivo porcine colon and a post mortem human in-situ colon. The OCT-rectoscope consistently distinguished various layers of the intestinal wall, identified gut-associated lymphatic tissue, and visualized a rectal polyp during the imaging procedure with 3D-reconstruction in real time. Subsequent histological examination confirmed these findings. The body donor was preserved using an ethanol-glycerol-lysoformin-based technique for true-to-life tissue consistency. We could demonstrate that the novel MHZ-OCT-rectoscope effectively discriminates rectal wall layers and crucial tissue characteristics in a post mortem human colon in-situ. This real-time-3D-OCT holds promise as a valuable future diagnostic tool for assessing disease state and therapy response on-site in rectal diseases.
Collapse
Affiliation(s)
- Berenice Schulte
- Interdisciplinary Endoscopy, Medical Department 1, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Madita Göb
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | | | - Simon Lotz
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | | | - Marvin Heimke
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Mario Pieper
- Institute of Anatomy, University of Luebeck, Luebeck, Germany
- Airway Research Center North (ARCN), German Center for Lung Research (DZL), Luebeck, Germany
| | - Tillmann Heinze
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Thilo Wedel
- Center of Clinical Anatomy, Institute of Anatomy, Christian-Albrechts University Kiel, Kiel, Germany
| | - Maik Rahlves
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Robert Huber
- Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany
| | - Mark Ellrichmann
- Interdisciplinary Endoscopy, Medical Department 1, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
| |
Collapse
|
2
|
Nawaz M, Uvaliyev A, Bibi K, Wei H, Abaxi SMD, Masood A, Shi P, Ho HP, Yuan W. Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review. Comput Med Imaging Graph 2023; 108:102269. [PMID: 37487362 DOI: 10.1016/j.compmedimag.2023.102269] [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: 05/03/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.
Collapse
Affiliation(s)
- Mehmood Nawaz
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Adilet Uvaliyev
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Khadija Bibi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Hao Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Sai Mu Dalike Abaxi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Anum Masood
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Peilun Shi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
| |
Collapse
|
3
|
Shi P, Qiu J, Abaxi SMD, Wei H, Lo FPW, Yuan W. Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13111947. [PMID: 37296799 DOI: 10.3390/diagnostics13111947] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics.
Collapse
Affiliation(s)
- Peilun Shi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Jianing Qiu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Sai Mu Dalike Abaxi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Hao Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Frank P-W Lo
- Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| |
Collapse
|
4
|
Durand T, Paul-Gilloteaux P, Gora M, Laboudie L, Coron E, Neveu I, Neunlist M, Naveilhan P. Visualizing enteric nervous system activity through dye-free dynamic full-field optical coherence tomography. Commun Biol 2023; 6:236. [PMID: 36864093 PMCID: PMC9981581 DOI: 10.1038/s42003-023-04593-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
Major advances have been achieved in imaging technologies but most methodological approaches currently used to study the enteric neuronal functions rely on exogenous contrast dyes that can interfere with cellular functions or survival. In the present paper, we investigated whether full-field optical coherence tomography (FFOCT), could be used to visualize and analyze the cells of the enteric nervous system. Experimental work on whole-mount preparations of unfixed mouse colons showed that FFOCT enables the visualization of the myenteric plexus network whereas dynamic FFOCT enables to visualize and identify in situ individual cells in the myenteric ganglia. Analyzes also showed that dynamic FFOCT signal could be modified by external stimuli such veratridine or changes in osmolarity. These data suggest that dynamic FFOCT could be of great interest to detect changes in the functions of enteric neurons and glia in normal and disease conditions.
Collapse
Affiliation(s)
- Tony Durand
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France
| | - Perrine Paul-Gilloteaux
- Nantes Université, CNRS, INSERM, l'institut du thorax, F-44000, Nantes, France
- Nantes Université, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UAR 3556, F-44000, Nantes, France
| | - Michalina Gora
- Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland
- ICube Laboratory, CNRS, Strasbourg University, Strasbourg, France
| | - Lara Laboudie
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France
| | - Emmanuel Coron
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France
- Department of Gastroenterology and Hepatology, University Hospital of Geneva (HUG), rue Gabrielle Perret-Gentil 4, 1211, Genève, 1205, Switzerland
| | - Isabelle Neveu
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France
| | - Michel Neunlist
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France.
| | - Philippe Naveilhan
- Nantes Université, CHU Nantes, Inserm, TENS, The Enteric Nervous System in Gut and Brain Diseases, IMAD, Nantes, France
| |
Collapse
|
5
|
Wang N, Lee CY, Park HC, Nauen DW, Chaichana KL, Quinones-Hinojosa A, Bettegowda C, Li X. Deep learning-based optical coherence tomography image analysis of human brain cancer. BIOMEDICAL OPTICS EXPRESS 2023; 14:81-88. [PMID: 36698668 PMCID: PMC9842008 DOI: 10.1364/boe.477311] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.
Collapse
Affiliation(s)
- Nathan Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Cheng-Yu Lee
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Hyeon-Cheol Park
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - David W. Nauen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | | | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| |
Collapse
|
6
|
Yuan W, Thiboutot J, Park HC, Li A, Loube J, Mitzner W, Yarmus L, Brown RH, Li X. Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT. IEEE Trans Biomed Eng 2022; PP:10.1109/TBME.2022.3188173. [PMID: 35786546 PMCID: PMC9842112 DOI: 10.1109/tbme.2022.3188173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE/BACKGROUND In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo. METHODS We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy. RESULTS The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method. CONCLUSION The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D. SIGNIFICANCE These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.
Collapse
Affiliation(s)
- Wu Yuan
- Johns Hopkins University, Baltimore, MD 21205, USA; Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Jeffrey Thiboutot
- Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Hyeon-cheol Park
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ang Li
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Jeffrey Loube
- Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Wayne Mitzner
- Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Lonny Yarmus
- Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Robert H. Brown
- Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Xingde Li
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| |
Collapse
|