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Proceedings Paper

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

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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150S (16 March 2020); doi: 10.1117/12.2549516
Show Author Affiliations
François-Xavier Carton, Univ. Grenoble Alpes, CNRS, Grenoble INP (France)
Vanderbilt Univ. (United States)
Matthieu Chabanas, Univ. Grenoble Alpes, CNRS, Grenoble INP (France)
Vanderbilt Univ. (United States)
Bodil K. R. Munkvold, Norwegian Univ. of Science and Technology (Norway)
Ingerid Reinertsen, SINTEF (Norway)
Jack H. Noble, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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