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Advances towards Intelligent Computer-Assisted Interventions: From Medical Imaging to Surgical...
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Advances towards Intelligent Computer-Assisted Interventions: From Medical Imaging to Surgical Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 5083

Special Issue Editors

Dr. Zhe Min

E-Mail Website
Guest Editor
Dept of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
Interests: medical robotics; point set registration; medical image registration; medical image segmentation; surgical navigation
Dr. Jie Luo

E-Mail Website
Guest Editor
Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: medical imaging; medical image registration; neuronavigation; theranostics; image-guided surgery

Special Issue Information

Dear Colleagues,

Over the past decade, artificial intelligence and robotics have instigated enormous changes to human societies and research fields. Intelligent and further automated robotic surgery is a promising research area that attracts more and more attention among researchers, due to its potential high accuracy and safety. However, there are still many theoretical and technical challenges in achieving this ultimate goal, including the accurate and fast diagnosis of the surgical organ/target, safe and fast pre-operative surgical planning, accurate and fast information fusion, robot/AR device calibration, etc.

This Special Issue therefore aims to collate both original research and review articles on recent advances in the theories, methodologies, and applications pertaining to fields of computer-assisted interventions. Both classical and deep-learning-based methods in fields of medical imaging and surgical robotics are welcomed.

Potential topics include, but are not limited to, the following:

  • Medical image segmentation;
  • Object detection in medical images;
  • Medical image registration/fusion;
  • Uncertainty modelling for medical imaging;
  • 2D/3D point cloud registration;
  • Hand-eye calibration algorithms;
  • X+Reality for computer-assisted intervention;
  • Surgical robotics;
  • Automated surgical instrument detection/segmentation;
  • Automated surgical phase recognition;
  • Advanced deep learning methods/theories for medical imaging.

Dr. Zhe Min
Dr. Jie Luo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical imaging
  • computer-assisted intervention
  • registration
  • segmentation
  • deep learning
  • surgical robotics

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Published Papers (2 papers)

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Research

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19 pages, 19790 KiB  
Article
Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks
by Jose L. Ruiz-Casado, Miguel A. Molina-Cabello and Rafael M. Luque-Baena
Sensors 2024, 24(12), 3777; https://doi.org/10.3390/s24123777 - 11 Jun 2024
Cited by 5 | Viewed by 1944
Abstract
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, [...] Read more.
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalizability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations such as translation or rotation to the images to increase variability in the dataset. Another possibility is using generative adversarial networks (GANs), which can generate images from a relatively small training set. This work aims to enhance model performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques. Full article
(This article belongs to the Special Issue Advances towards Intelligent Computer-Assisted Interventions: From Medical Imaging to Surgical Robotics)
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Review

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13 pages, 904 KiB  
Review
Advancing DIEP Flap Monitoring with Optical Imaging Techniques: A Narrative Review
by Hailey Hwiram Kim, In-Seok Song and Richard Jaepyeong Cha
Sensors 2024, 24(14), 4457; https://doi.org/10.3390/s24144457 - 10 Jul 2024
Cited by 2 | Viewed by 2391
Abstract
Objectives: This review aims to explore recent advancements in optical imaging techniques for monitoring the viability of Deep Inferior Epigastric Perforator (DIEP) flap reconstruction. The objectives include highlighting the principles, applications, and clinical utility of optical imaging modalities such as near-infrared spectroscopy (NIRS), [...] Read more.
Objectives: This review aims to explore recent advancements in optical imaging techniques for monitoring the viability of Deep Inferior Epigastric Perforator (DIEP) flap reconstruction. The objectives include highlighting the principles, applications, and clinical utility of optical imaging modalities such as near-infrared spectroscopy (NIRS), indocyanine green (ICG) fluorescence angiography, laser speckle contrast imaging (LSCI), hyperspectral imaging (HSI), dynamic infrared thermography (DIRT), and short-wave infrared thermography (SWIR) in assessing tissue perfusion and oxygenation. Additionally, this review aims to discuss the potential of these techniques in enhancing surgical outcomes by enabling timely intervention in cases of compromised flap perfusion. Materials and Methods: A comprehensive literature review was conducted to identify studies focusing on optical imaging techniques for monitoring DIEP flap viability. We searched PubMed, MEDLINE, and relevant databases, including Google Scholar, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ProQuest Dissertations & Theses, among others, using specific keywords related to optical imaging, DIEP flap reconstruction, tissue perfusion, and surgical outcomes. This extensive search ensured we gathered comprehensive data for our analysis. Articles discussing the principles, applications, and clinical use of NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR in DIEP flap monitoring were selected for inclusion. Data regarding the techniques’ effectiveness, advantages, limitations, and potential impact on surgical decision-making were extracted and synthesized. Results: Optical imaging modalities, including NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation in DIEP flap reconstruction. These techniques provide objective and quantitative data, enabling surgeons to monitor flap viability accurately. Studies have demonstrated the effectiveness of optical imaging in detecting compromised perfusion and facilitating timely intervention, thereby reducing the risk of flap complications such as partial or total loss. Furthermore, optical imaging modalities have shown promise in improving surgical outcomes by guiding intraoperative decision-making and optimizing patient care. Conclusions: Recent advancements in optical imaging techniques present valuable tools for monitoring the viability of DIEP flap reconstruction. NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation, enabling accurate evaluation of flap viability. These modalities have the potential to enhance surgical outcomes by facilitating timely intervention in cases of compromised perfusion, thereby reducing the risk of flap complications. Incorporating optical imaging into clinical practice can provide surgeons with objective and quantitative data, assisting in informed decision-making for optimal patient care in DIEP flap reconstruction surgeries. Full article
(This article belongs to the Special Issue Advances towards Intelligent Computer-Assisted Interventions: From Medical Imaging to Surgical Robotics)
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