Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery

Abstract

Knowledge of the exact tumor location and structures at risk in its vicinity are crucial for neurosurgical interventions. Neuronavigation systems support navigation within the patient's brain, based on preoperative MRI (preMRI). However, increasing tissue deformation during the course of tumor resection reduces navigation accuracy based on preMRI. Intraoperative ultrasound (iUS) is therefore used as real-time intraoperative imaging. Registration of preMRI and iUS remains a challenge due to different or varying contrasts in iUS and preMRI. Here, we present an automatic and efficient segmentation of B-mode US images to support the registration process. The falx cerebri and the tentorium cerebelli were identified as examples for central cerebral structures and their segmentations can serve as guiding frame for multi-modal image registration. Segmentations of the falx and tentorium were performed with an average Dice coefficient of 0.74 and an average Hausdorff distance of 12.2 mm. The subsequent registration incorporates these segmentations and increases accuracy, robustness and speed of the overall registration process compared to purely intensity-based registration. For validation an expert manually located corresponding landmarks. Our approach reduces the initial mean Target Registration Error from 16.9 mm to 3.8 mm using our intensity-based registration and to 2.2 mm with our combined segmentation and registration approach. The intensity-based registration reduced the maximum initial TRE from 19.4 mm to 5.6 mm, with the approach incorporating segmentations this is reduced to 3.0 mm. Mean volumetric intensity-based registration of preMRI and iUS took 40.5 s, including segmentations 12.0 s.

Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery

“…Given the fact that our annotations are not publicly available, only a qualitative comparison is made with respect to other methods which also proposed a US segmentation solution in the context of neurosurgery [27] [28] [30] [33].…”

“…Furthermore, from a qualitative comparison with other segmentation methods involving US data, we can highlight some advances of our approach. First of all, with respect to [27] [33], a higher number of anatomical structures is included in our manual annotations. Therefore, the potential range of clinical scenarios in which our method could be applied might be wider.…”

“…Our solution proposes a segmentation-based registration approach to register US volumes acquired at different stages of neurosurgical procedures and compensate brain shift. A few approaches already applied segmentation methods on US data to then register MRI and iUS [27] [28]. Our solution represents the first segmentation-based method aimed at US-US volumes registration.…”

Reliable navigation registration in cranial and spine surgery based on intraoperative computed tomography

“…Such an image registration based on 3D ultrasound images is still challenging and might need additional nonlinear components; nevertheless, in a recent study an initial mean TRE measuring the image registration accuracy could be reduced from 16.9 to 3.8 mm using an intensity-based registration and to 2.2 mm with a combined segmentation and registration approach. 21 Of course, one might ask whether registration errors, as low as we achieved with the automatic iCT-based registration of less than 1 mm, are necessary at all in different kinds of surgery, justifying the effort of intraoperative imaging in general and, in case of iCT, the radiation exposure for the patient in particular. Since a reliable registration is probably the most important, but not only, step in the entire pipeline of registration, it is crucial to minimize each factor contributing to inaccuracy.…”

Brain Tumor Detection using Image Segmentation Techniques on MRI Images

“…Automatic and efficient MRI-US segmentation for improving intraoperative image fusion in image-guided neurosurgery [1] This paper title present the work on the image processing, registration(in real time) and segmentation. It uses the algorithm called B-mode US image for the efficient segmentation and registration process.…”