Abstract
Automatic localization and segmentation of the tumor and resection cavity in intraoperative ultrasound images can assist in accurate navigation during image-guided surgery. In this study, we benchmark a pyramidal blur-pooled 2D U-Net as a baseline method to segment the tumor and resection cavity before, during, and after resection in 3D intraoperative ultrasound images. Slicing the 3D image along three transverse, sagittal, and coronal axes, we train a different model corresponding to each axis and average three predicted masks to obtain the final prediction. It is demonstrated that the averaged mask consistently achieves a Dice score greater than or equal to each individual mask predicted by only one model along one axis.
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References
Dixon, L., Lim, A., Grech-Sollars, M., Nandi, D., Camp, S.: Intraoperative ultrasound in brain tumor surgery: A review and implementation guide. Neurosurg. Rev. 45(4), 2503–2515 (2022)
Xiao, Y., Fortin, M., Unsgärd, G., Rivaz, H., Reinertsen, I.: REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries: A. Med. Phys. 44(7), 3875–3882 (2017)
Behboodi, B., et al.: RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images. (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci. 9351, 234–241 (2015)
Sharifzadeh, M., Benali, H., Rivaz, H.: Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 69(5), 1703–1713 (2022)
Sharifzadeh, M., Benali, H., Rivaz, H.: Shift-Invariant Segmentation in Breast Ultrasound Images. IEEE International Ultrasonics Symposium, IUS (2021)
Abraham, N., Khan, N.M.: A novel focal Tversky loss function with improved attention u-net for lesion segmentation. In: Proceedings - International Symposium on Biomedical Imaging, (ISBI), pp. 683–687 (2019)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR (2019)
Adam Paszke, A.: PyTorch: an imperative style, high-performance deep learning library. In Adv. Neural Inf. Proc. Syst. 32 (2019)
Carton, F.-X., Chabanas, M., Le Lann, F., Noble, J.H.: Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net. J. Med. Imaging 7(03), 1 (2020)
Carton, F.-X., Noble, J.H., Chabanas, M.: Automatic segmentation of brain tumor resections in intraoperative ultrasound images. In Fei, B., Linte, C.A., eds, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. Society of Photo-Optical Instrumentation Engineers(SPIE) 7, p. 104 (2019)
Sharifzadeh, M., Benali, H., Rivaz, H.: An Ultra-Fast Method for Simulation of Realistic Ultrasound Images. IEEE International Ultrasonics Symposium, IUS (2021)
Sharifzadeh, M., Tehrani, A.K.Z., Benali, H., Rivaz, H.: Ultrasound Domain Adaptation Using Frequency Domain Analysis. In: IEEE International Ultrasonics Symposium (IUS), pp. 1–4(2021)
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We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Sharifzadeh, M., Benali, H., Rivaz, H. (2023). Segmentation of Intraoperative 3D Ultrasound Images Using a Pyramidal Blur-Pooled 2D U-Net. In: Xiao, Y., Yang, G., Song, S. (eds) Lesion Segmentation in Surgical and Diagnostic Applications. CuRIOUS KiPA MELA 2022 2022 2022. Lecture Notes in Computer Science, vol 13648. Springer, Cham. https://doi.org/10.1007/978-3-031-27324-7_9
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DOI: https://doi.org/10.1007/978-3-031-27324-7_9
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