The brain is significantly deformed during neurosurgery, in particular because of the removal of tumor tissue. Because of this deformation, intraoperative data is needed for accurate navigation in image-guided surgery. During the surgery, it is easier to acquire ultrasound images than Magnetic Resonance (MR) images. However, ultrasound images are difficult to interpret. Several methods have been developed to register preoperative MR and intraoperative ultrasound images, to allow accurate navigation during neurosurgery. Model-based methods need the location of the resection cavity to take into account the tissue removal in the model. Manually segmenting this cavity is extremely time consuming and cannot be performed in the operating room. It is also difficult and error-prone because of the noise and reconstruction artifacts in the ultrasound images. In this work, we present a method to perform the segmentation of the resection cavity automatically. We manually labelled the resection cavity on the ultrasound volumes from a database of 23 patients. We trained a Unet-based artificial neural network with our manual segmentation and evaluated several variations of the method. Our best method results in 0.82 mean Dice score over the 10 testing cases. The Dice scores range from 0.67 to 0.96, and eight out of ten are higher than 0.75. For the most difficult test cases, lacking clear contour, the manual segmentation is also difficult but our method still yields acceptable results. Overall the segmentations obtained with the automatic methods are qualitatively similar to the manual ones.
François-Xavier Carton, Jack H. Noble, and Matthieu Chabanas, "Automatic segmentation of brain tumor resections in intraoperative ultrasound images," Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109510U (Presented at SPIE Medical Imaging: February 18, 2019; Published: 8 March 2019); https://doi.org/10.1117/12.2513613.
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