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

François-Xavier Carton; Matthieu Chabanas; Bodil K. R. Munkvold; Ingerid Reinertsen; Jack H. Noble

doi:10.1117/12.2549516

16 March 2020 Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

François-Xavier Carton, Matthieu Chabanas, Bodil K. R. Munkvold, Ingerid Reinertsen, Jack H. Noble

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113150S (2020) https://doi.org/10.1117/12.2549516
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Citation Download Citation

François-Xavier Carton, Matthieu Chabanas, Bodil K. R. Munkvold, Ingerid Reinertsen, and Jack H. Noble "Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150S (16 March 2020); https://doi.org/10.1117/12.2549516

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