Tumor Volume Assessment in Low-Grade Gliomas: A Comparison of Preoperative Magnetic Resonance Imaging to Coregistered Intraoperative 3-Dimensional Ultrasound Recordings

Munkvold, Bodil Karoline Ravn; Bø, Hans Kristian; Jakola, Asgeir Store; Reinertsen, Ingerid; Berntsen, Erik Magnus; Unsgård, Geirmund; Torp, Sverre Helge; Solheim, Ole

doi:10.1093/neuros/nyx392

Cited by 14 publications

(9 citation statements)

References 28 publications

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“…The intra-rater tumor volume difference for the ground truth was reported in. 14 This same volume difference was then computed between the ground truth and the segmentations estimated with the 3D network. The two outliers were excluded because comparing non-overlaping volumes is meaningless.…”

Section: Results For the 3-d Networkmentioning

confidence: 99%

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Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

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Chabanas

Munkvold

et al. 2020

Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

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Because of the deformation of the brain during neurosurgery, intraoperative imaging can be used to visualize the actual location of the brain structures. These images are used for image-guided navigation as well as determining whether the resection is complete and localizing the remaining tumor tissue. Intraoperative ultrasound (iUS) is a convenient modality with short acquisition times. However, iUS images are difficult to interpret because of the noise and artifacts. In particular, tumor tissue is difficult to distinguish from healthy tissue and it is very difficult to delimit tumors in iUS images. In this paper, we propose an automatic method to segment low grade brain tumors in iUS images using a 2-D and 3-D U-Net. We trained the networks on three folds with twelve training cases and five test cases each. The obtained results are promising, with a median Dice score of 0.72. The volume differences between the estimated and ground truth segmentations were similar to the intra-rater volume differences. While these results are preliminary, they suggest that deep learning methods can be successfully applied to tumor segmentation in intraoperative images.

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Section: Results For the 3-d Networkmentioning

confidence: 99%

“…For each patient, two pMR volumes (T1w and T2-FLAIR scans) and three iUS 3-D volumes were acquired before, during, and after resection. We used as the ground truth (GT) the manual segmentations by Munkvold et al, 14 which were available for 17 volumes before resection. Figure 1 shows examples of ground truth segmentations for three cases.…”

Section: Datamentioning

confidence: 99%

Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

Carton

Chabanas

Munkvold

et al. 2020

Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

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“…For the universal AVQ method proposed here, the first step is to convert DICOM data into a 3D model. Since 3D Slicer has been commonly used in various medical and bioengineering fields, we elected to use it as the reference method 1,11,12,20,31) . Although 3D Slicer has been previously used to define cerebral vascular anatomy and obtain vascular parameters, to our knowledge, it has not been used to measure aneurysm volume 4,[13][14][15]18,19) .…”

Section: Discussionmentioning

confidence: 99%

An Efficient Method for Aneurysm Volume Quantification Applicable in Any Shape and Modalities

Chung

2021

J Korean Neurosurg Soc

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Objective : Aneurysm volume quantification (AVQ) using the equation of ellipsoid volume is widely used although it is inaccurate. Furthermore, AVQ with 3-dimensional (3D) rendered data has limitations in general use. A novel universal method for AVQ is introduced for any diagnostic modality and application to any shape of aneurysms. Methods : Relevant AVQ studies published from January 1997 to June 2019 were identified to determine common methods of AVQ. The basic idea is to eliminate the normal artery volume from 3D model with the aneurysm. After Digital Imaging and Communications in Medicine (DICOM) data is converted and exported to stereolithography (STL) file format, the 3D STL model is modified to remove the aneurysm and the volume difference between the 3D model with/without the aneurysm is defined as the aneurysm volume. Fifty randomly selected aneurysms from DICOM database were used to validate the different AVQ methods. Results : We reviewed and categorized AVQ methods in 121 studies. Approximately 60% used the ellipsoid method, while 24% used the 3D model. For 50 randomly selected aneurysms, volumes were measured using 3D Slicer, RadiAnt, and ellipsoid method. Using 3D Slicer as the reference, the ratios of mean difference to mean volume obtained by RadiAnt and ellipsoid method were -1.21±7.46% and 4.04±30.54%, respectively. The deviations between RadiAnt and 3D Slicer were small despite of aneurysm shapes, but those of ellipsoid method and 3D Slicer were large. Conclusion :In spite of inaccuracy, ellipsoid method is still mostly used. We propose a novel universal method for AVQ that is valid, low cost, and easy to use.

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“…Nevertheless, the newest and perhaps the most detailed data presented by leading Norwegian group showed that MRI is superior in pre-resectional glioma visualization ( 18 ) as well as in visualization of small tumor remnants ( 19 ). This finding is important ( Figure 1 ) despite the fact that in some cases glioma tissue may be better visualized by IOUS, as compared to high-field MRI ( Figure 2 ).…”

Section: Suboptimal Ultrasound Imagingmentioning

confidence: 99%

Current Limitations of Intraoperative Ultrasound in Brain Tumor Surgery

et al. 2021

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While benefits of intraoperative ultrasound (IOUS) have been frequently described, data on IOUS limitations are relatively sparse. Suboptimal ultrasound imaging of some pathologies, various types of ultrasound artifacts, challenging patient positioning during some IOUS-guided surgeries, and absence of an optimal IOUS probe depicting the entire sellar region during transsphenoidal pituitary surgery are some of the most important pitfalls. This review aims to summarize prominent limitations of current IOUS systems, and to present possibilities to reduce them by using ultrasound technology suitable for a specific procedure and by proper scanning techniques. In addition, future trends of IOUS imaging optimization are described in this article.

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Tumor Volume Assessment in Low-Grade Gliomas: A Comparison of Preoperative Magnetic Resonance Imaging to Coregistered Intraoperative 3-Dimensional Ultrasound Recordings

Abstract: The tumor volumes of LGGs segmented from intraoperative US images were most often smaller than the tumor volumes segmented from preoperative MRIs. There was a much better match between the 2 modalities in astrocytomas.

Cited by 14 publications

References 28 publications

Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

An Efficient Method for Aneurysm Volume Quantification Applicable in Any Shape and Modalities

Current Limitations of Intraoperative Ultrasound in Brain Tumor Surgery

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