Dataset Information

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

ABSTRACT: Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent-free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning-based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.

SUBMITTER: Ayala L

PROVIDER: S-EPMC10005169 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Ayala Leonardo L Adler Tim J TJ Seidlitz Silvia S Wirkert Sebastian S Engels Christina C Seitel Alexander A Sellner Jan J Aksenov Alexey A Bodenbach Matthias M Bader Pia P Baron Sebastian S Vemuri Anant A Wiesenfarth Manuel M Schreck Nicholas N Mindroc Diana D Tizabi Minu M Pirmann Sebastian S Everitt Brittaney B Kopp-Schneider Annette A Teber Dogu D Maier-Hein Lena L

Science advances 20230310 10

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a ...[more]

PMID: 36897951

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Project description:BackgroundAnastomotic leakage (AL) is a dreaded complication following colorectal cancer surgery, impacting patient outcome and leads to increasing healthcare consumption as well as economic burden. Bowel perfusion is a significant modifiable factor for anastomotic healing and thus crucial for reducing AL.AimsThe study aimed to calculate a cut-off value for quantified laser speckle perfusion units (LSPUs) in order to differentiate between ischemic and well-perfused tissue and to assess inter-observer reliability.MethodsLSCI was performed using a porcine ischemic small bowel loop model with the PerfusiX-Imaging® system. An ischemic area, a well-perfused area, and watershed areas, were selected based on the LSCI colormap. Subsequently, local capillary lactate (LCL) levels were measured. A logarithmic curve estimation tested the correlation between LSPU and LCL levels. A cut-off value for LSPU and lactate was calculated, based on anatomically ischemic and well-perfused tissue. Inter-observer variability analysis was performed with 10 observers.ResultsDirectly after ligation of the mesenteric arteries, differences in LSPU values between ischemic and well-perfused tissue were significant (p < 0.001) and increased significantly throughout all following measurements. LCL levels were significantly different (p < 0.001) at both 60 and 120 min. Logarithmic curve estimation showed an R2 value of 0.56 between LSPU and LCL values. A LSPU cut-off value was determined at 69, with a sensitivity of 0.94 and specificity of 0.87. A LCL cut-off value of 3.8 mmol/L was found, with a sensitivity and specificity of 0.97 and 1.0, respectively. There was no difference in assessment between experienced and unexperienced observers. Cohen's Kappa values were moderate to good (0.52-0.66).ConclusionReal-time quantification of LSPUs may be a feasible intraoperative method to assess tissue perfusion and a cut-off value could be determined with high sensitivity and specificity. Inter-observer variability was moderate to good, irrespective of prior experience with the technique.

Project description:ObjectivesCurrently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges.MethodsThe study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy.ResultsOverall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model.ConclusionWe have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.

Dataset Information

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Publications

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

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