Dataset Information

A comparative analysis using flowmeter, laser-Doppler |spectrophotometry, and indocyanine green-videoangiography for detection of vascular stenosis in free flaps.

ABSTRACT: The effects of gradual vascular occlusion on the blood supply of perfused areas are poorly described. Information relating to the comparison of flap monitoring techniques is lacking. Varying stenotic conditions (0%, 25%, 50%, 75% and 100%) were generated on purpose at the A. and V. femoralis in the rat model. Analyses included flowmeter, simultaneous laser-Doppler flowmetry and tissue spectrophotometry (O2C) and indocyanine green- (ICG-) videoangiography with integrated FLOW 800 tool. A Random Forests prediction model was used to analyse the importance of each method to diagnose the stenotic conditions. The ability to discriminate and to accurately estimate the probability of stenosis was assessed by Receiver Operating Characteristic (ROC) curves and calibration plots. Blood flow changes for all modalities were described in detail. Flowmeter displayed earliest a linear decrease as a result of increasing stenosis. A stenosis of 50% degrees was most difficult to detect correctly. The combination of flowmeter and ICG-videoangiography showed high diagnostic power for each stenotic situation (area under the ROC?>?0.79). Flowmeter and ICG-videoangiography showed to be most relevant in detection of varying stenotic conditions and may change the clinical outcome. The O2C showed less effect on varying stenotic situations as the only surface monitoring device.

SUBMITTER: Mucke T

PROVIDER: S-EPMC6976589 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

A comparative analysis using flowmeter, laser-Doppler |spectrophotometry, and indocyanine green-videoangiography for detection of vascular stenosis in free flaps.

Mücke Thomas T Hapfelmeier Alexander A Schmidt Leonard H LH Fichter Andreas M AM Kanatas Anastasios A Wolff Klaus-Dietrich KD Ritschl Lucas M LM

Scientific reports 20200122 1

The effects of gradual vascular occlusion on the blood supply of perfused areas are poorly described. Information relating to the comparison of flap monitoring techniques is lacking. Varying stenotic conditions (0%, 25%, 50%, 75% and 100%) were generated on purpose at the A. and V. femoralis in the rat model. Analyses included flowmeter, simultaneous laser-Doppler flowmetry and tissue spectrophotometry (O2C) and indocyanine green- (ICG-) videoangiography with integrated FLOW 800 tool. A Random F ...[more]

PMID: 31969630

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Project description:There have been few anatomical structure segmentation studies using deep learning. Numbers of training and ground truth images applied were small and the accuracies of which were low or inconsistent. For a surgical video anatomy analysis, various obstacles, including a variable fast-changing view, large deformations, occlusions, low illumination, and inadequate focus occur. In addition, it is difficult and costly to obtain a large and accurate dataset on operational video anatomical structures, including arteries. In this study, we investigated cerebral artery segmentation using an automatic ground-truth generation method. Indocyanine green (ICG) fluorescence intraoperative cerebral videoangiography was used to create a ground-truth dataset mainly for cerebral arteries and partly for cerebral blood vessels, including veins. Four different neural network models were trained using the dataset and compared. Before augmentation, 35,975 training images and 11,266 validation images were used. After augmentation, 260,499 training and 90,129 validation images were used. A Dice score of 79% for cerebral artery segmentation was achieved using the DeepLabv3+ model trained using an automatically generated dataset. Strict validation in different patient groups was conducted. Arteries were also discerned from the veins using the ICG videoangiography phase. We achieved fair accuracy, which demonstrated the appropriateness of the methodology. This study proved the feasibility of operating field view of the cerebral artery segmentation using deep learning, and the effectiveness of the automatic blood vessel ground truth generation method using ICG fluorescence videoangiography. Using this method, computer vision can discern blood vessels and arteries from veins in a neurosurgical microscope field of view. Thus, this technique is essential for neurosurgical field vessel anatomy-based navigation. In addition, surgical assistance, safety, and autonomous surgery neurorobotics that can detect or manipulate cerebral vessels would require computer vision to identify blood vessels and arteries.

Dataset Information

A comparative analysis using flowmeter, laser-Doppler |spectrophotometry, and indocyanine green-videoangiography for detection of vascular stenosis in free flaps.

Publications

A comparative analysis using flowmeter, laser-Doppler |spectrophotometry, and indocyanine green-videoangiography for detection of vascular stenosis in free flaps.

Similar Datasets

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