Automatic segmentation of brain tumor resections in intraoperative ultrasound images

Abstract

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 0.86 mean Dice score over the 10 testing cases. The Dice scores range from 0.68 to 0.96, and nine 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.

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

“…We first tested a 2-D version which was applied to iUS images to segment the resection cavity in brain tumor surgeries in another study. 12 We trained two networks 2D-1 and 2D-9 with one and nine adjacent slices as input, respectively. Input size was 256x256, so we used a sliding window (with a stride of 64) for the training and testing phases.…”

Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures

“…We expect the proposed registration approach to achieve better results than the same method not excluding the resection cavity. Regarding the segmentation step, to the best of our knowledge only the authors in [20,21] proposed a solution for this task. In [21], they described a method based on a 2D U-Net to segment the resection cavity in US volumes.…”

“…Regarding the segmentation step, to the best of our knowledge only the authors in [20,21] proposed a solution for this task. In [21], they described a method based on a 2D U-Net to segment the resection cavity in US volumes. Besides, in [20] they also demonstrated that the 3D architecture achieves better results than a 2D approach.…”