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Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging

PURPOSE: Intraoperative evaluation of bowel perfusion is currently dependent upon subjective assessment. Thus, quantitative and objective methods of bowel viability in intestinal anastomosis are scarce. To address this clinical need, a conditional adversarial network is used to analyze the data from...

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Autores principales: Wang, Yaning, Tiusaba, Laura, Jacobs, Shimon, Saruwatari, Michele, Ning, Bo, Levitt, Marc, Sandler, Anthony D., Nam, So-Hyun, Kang, Jin U., Cha, Jaepyeong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704416/
https://www.ncbi.nlm.nih.gov/pubmed/36466077
http://dx.doi.org/10.1117/1.JMI.9.6.064502
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author Wang, Yaning
Tiusaba, Laura
Jacobs, Shimon
Saruwatari, Michele
Ning, Bo
Levitt, Marc
Sandler, Anthony D.
Nam, So-Hyun
Kang, Jin U.
Cha, Jaepyeong
author_facet Wang, Yaning
Tiusaba, Laura
Jacobs, Shimon
Saruwatari, Michele
Ning, Bo
Levitt, Marc
Sandler, Anthony D.
Nam, So-Hyun
Kang, Jin U.
Cha, Jaepyeong
author_sort Wang, Yaning
collection PubMed
description PURPOSE: Intraoperative evaluation of bowel perfusion is currently dependent upon subjective assessment. Thus, quantitative and objective methods of bowel viability in intestinal anastomosis are scarce. To address this clinical need, a conditional adversarial network is used to analyze the data from laser speckle contrast imaging (LSCI) paired with a visible-light camera to identify abnormal tissue perfusion regions. APPROACH: Our vision platform was based on a dual-modality bench-top imaging system with red-green-blue (RGB) and dye-free LSCI channels. Swine model studies were conducted to collect data on bowel mesenteric vascular structures with normal/abnormal microvascular perfusion to construct the control or experimental group. Subsequently, a deep-learning model based on a conditional generative adversarial network (cGAN) was utilized to perform dual-modality image alignment and learn the distribution of normal datasets for training. Thereafter, abnormal datasets were fed into the predictive model for testing. Ischemic bowel regions could be detected by monitoring the erroneous reconstruction from the latent space. The main advantage is that it is unsupervised and does not require subjective manual annotations. Compared with the conventional qualitative LSCI technique, it provides well-defined segmentation results for different levels of ischemia. RESULTS: We demonstrated that our model could accurately segment the ischemic intestine images, with a Dice coefficient and accuracy of 90.77% and 93.06%, respectively, in 2560 RGB/LSCI image pairs. The ground truth was labeled by multiple and independent estimations, combining the surgeons’ annotations with fastest gradient descent in suspicious areas of vascular images. The total processing time was 0.05 s for an image size of [Formula: see text]. CONCLUSIONS: The proposed cGAN can provide pixel-wise and dye-free quantitative analysis of intestinal perfusion, which is an ideal supplement to the traditional LSCI technique. It has potential to help surgeons increase the accuracy of intraoperative diagnosis and improve clinical outcomes of mesenteric ischemia and other gastrointestinal surgeries.
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spelling pubmed-97044162023-11-28 Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging Wang, Yaning Tiusaba, Laura Jacobs, Shimon Saruwatari, Michele Ning, Bo Levitt, Marc Sandler, Anthony D. Nam, So-Hyun Kang, Jin U. Cha, Jaepyeong J Med Imaging (Bellingham) Computer-Aided Diagnosis PURPOSE: Intraoperative evaluation of bowel perfusion is currently dependent upon subjective assessment. Thus, quantitative and objective methods of bowel viability in intestinal anastomosis are scarce. To address this clinical need, a conditional adversarial network is used to analyze the data from laser speckle contrast imaging (LSCI) paired with a visible-light camera to identify abnormal tissue perfusion regions. APPROACH: Our vision platform was based on a dual-modality bench-top imaging system with red-green-blue (RGB) and dye-free LSCI channels. Swine model studies were conducted to collect data on bowel mesenteric vascular structures with normal/abnormal microvascular perfusion to construct the control or experimental group. Subsequently, a deep-learning model based on a conditional generative adversarial network (cGAN) was utilized to perform dual-modality image alignment and learn the distribution of normal datasets for training. Thereafter, abnormal datasets were fed into the predictive model for testing. Ischemic bowel regions could be detected by monitoring the erroneous reconstruction from the latent space. The main advantage is that it is unsupervised and does not require subjective manual annotations. Compared with the conventional qualitative LSCI technique, it provides well-defined segmentation results for different levels of ischemia. RESULTS: We demonstrated that our model could accurately segment the ischemic intestine images, with a Dice coefficient and accuracy of 90.77% and 93.06%, respectively, in 2560 RGB/LSCI image pairs. The ground truth was labeled by multiple and independent estimations, combining the surgeons’ annotations with fastest gradient descent in suspicious areas of vascular images. The total processing time was 0.05 s for an image size of [Formula: see text]. CONCLUSIONS: The proposed cGAN can provide pixel-wise and dye-free quantitative analysis of intestinal perfusion, which is an ideal supplement to the traditional LSCI technique. It has potential to help surgeons increase the accuracy of intraoperative diagnosis and improve clinical outcomes of mesenteric ischemia and other gastrointestinal surgeries. Society of Photo-Optical Instrumentation Engineers 2022-11-28 2022-11 /pmc/articles/PMC9704416/ /pubmed/36466077 http://dx.doi.org/10.1117/1.JMI.9.6.064502 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Computer-Aided Diagnosis
Wang, Yaning
Tiusaba, Laura
Jacobs, Shimon
Saruwatari, Michele
Ning, Bo
Levitt, Marc
Sandler, Anthony D.
Nam, So-Hyun
Kang, Jin U.
Cha, Jaepyeong
Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title_full Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title_fullStr Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title_full_unstemmed Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title_short Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
title_sort unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging
topic Computer-Aided Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704416/
https://www.ncbi.nlm.nih.gov/pubmed/36466077
http://dx.doi.org/10.1117/1.JMI.9.6.064502
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