Dataset Information

Intraoperative dynamic assessment of the posterior communicating artery and its branches by indocyanine green videoangiography.

ABSTRACT:

Background

True hemodynamic assessment of the posterior communicating artery (PComA) by preoperative angiography in terms of its perforators and configuration (adult vs. fetal vs. transitional) can be challenging in the surgical treatment of aneurysms involving the PComA, posterior cerebral artery, and basilar artery. Indocyanine green videoangiography (ICG-VA) is a widely accepted new technique in the surgical treatment of intracranial aneurysms to assess the patency of the parent artery, branches, and residual flow within the aneurysm after clipping.

Case description

Here we report two cases in which ICG-VA was utilized to assess either the direction of flow in the PComA or preservation of the PComA perforators with temporary clip application before dividing the PComA.

Conclusions

Our experience is that ICG-VA can be used to assess the main trunk, and perforating branches of the PComA providing real-time, dynamic intraoperative information of the surgical field. Therefore we suggest that ICG-VA may increase the safety of surgical treatment of aneurysm involving PComA.

SUBMITTER: Gurer B

PROVIDER: S-EPMC3815034 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Intraoperative dynamic assessment of the posterior communicating artery and its branches by indocyanine green videoangiography.

Gürer Bora B Antar Veysel V Cikla Ulas U Bauer Andrew A Baskaya Mustafa K MK

Surgical neurology international 20130925

<h4>Background</h4>True hemodynamic assessment of the posterior communicating artery (PComA) by preoperative angiography in terms of its perforators and configuration (adult vs. fetal vs. transitional) can be challenging in the surgical treatment of aneurysms involving the PComA, posterior cerebral artery, and basilar artery. Indocyanine green videoangiography (ICG-VA) is a widely accepted new technique in the surgical treatment of intracranial aneurysms to assess the patency of the parent arter ...[more]

PMID: 24232934

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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

Intraoperative dynamic assessment of the posterior communicating artery and its branches by indocyanine green videoangiography.

Background

Case description

Conclusions

Publications

Intraoperative dynamic assessment of the posterior communicating artery and its branches by indocyanine green videoangiography.

Similar Datasets

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