Surgical spectral imaging

Clancy, Neil T.; Jones, Geoffrey; Maier-Hein, Lena; Elson, Daniel S.; Stoyanov, Danail

doi:10.1016/j.media.2020.101699

Cited by 103 publications

(116 citation statements)

References 181 publications

(224 reference statements)

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“…Finally, this short and succinct review has focused on research directions that are in active development. Due to limitations of space, we have not discussed opportunities around using computer vision with different imaging systems or spectral imaging despite the opportunities in AI systems to resolve ill posed inverse problems in that domain [58]. Additionally, we have not covered in detail work in vision for the entire OR but this is a very active area of development with exciting potential for a wider team workflow understanding [3].…”

Section: Discussionmentioning

confidence: 99%

Computer Vision in the Surgical Operating Room

et al. 2020

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Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic.

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Section: Discussionmentioning

confidence: 99%

Computer Vision in the Surgical Operating Room

et al. 2020

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“…Intraoperative hyperspectral imaging, artificial intelligence, deep machine learning, augmented reality, fusion of preoperative CT- or MRI-scans to IOUS and the operation field, and other technical developments will be possible in the near future using the robotic system, enabling new perspectives in hepato-biliary surgery and hopefully achieving better oncological results and patient survival [ 1 , 4 , 8 , 22 , 32 , 36 , 37 , 38 , 39 , 40 ]. Until then, ICG staining may serve as an additional helpful intraoperative aid in patients undergoing robot-assisted, atypical liver resection, providing real-time images and helping to plan intraoperatively and perform surgery in an individual, tissue-sparing way.…”

Section: Discussionmentioning

confidence: 99%

Usability of Indocyanine Green in Robot-Assisted Hepatic Surgery

Mehdorn

Beckmann

Braun

et al. 2021

JCM

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Recent developments in robotic surgery have led to an increasing number of robot-assisted hepatobiliary procedures. However, a limitation of robotic surgery is the missing haptic feedback. The fluorescent dye indocyanine green (ICG) may help in this context, which accumulates in hepatocellular cancers and around hepatic metastasis. ICG accumulation may be visualized by a near-infrared camera integrated into some robotic systems, helping to perform surgery more accurately. We aimed to test the feasibility of preoperative ICG application and its intraoperative use in patients suffering from hepatocellular carcinoma and metastasis of colorectal cancer, but also of other origins. In a single-arm, single-center feasibility study, we tested preoperative ICG application and its intraoperative use in patients undergoing robot-assisted hepatic resections. Twenty patients were included in the final analysis. ICG staining helped in most cases by detecting a clear lesion or additional metastases or when performing an R0 resection. However, it has limitations if applied too late before surgery and in patients suffering from severe liver cirrhosis. ICG staining may serve as a beneficial intraoperative aid in patients undergoing robot-assisted hepatic surgery. Dose and time of application and standardized fluorescence intensity need to be further determined.

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“…(iv) The valuable wavelengths for the identification of tumors could be selected from NIR-HSI data using resected specimens. In this study, OTN-NIR MSI was investigated using commercially available filters via straightforward construction; however, spectral images with high wavelength resolution could be obtained by adopting an acoustic tunable filter (AOTF) and a liquid crystal tunable filter to select the wavelength before imaging or light output [21,29,30]. In particular, a combination of an AOTF and a supercontinuum light source is promising because high power output and fast wavelength switching can be obtained.…”

Section: Discussionmentioning

confidence: 99%

“…The results indicate that multispectral imaging (MSI) using selected wavelengths in the range of OTN-NIR could potentially recognize invisible cancer and organs during surgery without labeling with a fluorescent probe. However, to date, only studies on laparoscopic HSI or MSI using less than 1000 nm light have been reported [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

Takamatsu

Kitagawa

Akimoto

et al. 2021

Sensors

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In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.

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Surgical spectral imaging

Cited by 103 publications

References 181 publications

Computer Vision in the Surgical Operating Room

Computer Vision in the Surgical Operating Room

Usability of Indocyanine Green in Robot-Assisted Hepatic Surgery

Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

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