Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid Ibn; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra J.; Wells, William M.

doi:10.1007/s11548-018-1786-7

Cited by 47 publications

(42 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Для решения задачи повышения точности цифровых моделей и развития автоматических методов выделения биологических тканей используется совместное использование различных методов диагностики [7,8]. В настоящее время проводятся исследования по использованию различных методов ультразвуковой диагностики, которая является более безопасной альтернативой рентгеновским лучам для визуализации и сегментации биологических тканей [9][10][11][12].…”

Section: проблемы формирования цифровых моделей по графической информunclassified

Преобразование Данных Лазерной Ультразвуковой Томографии В Медицинский Формат DICOM

Novikov¹,

Reshetov²,

Симонова³

et al. 2020

Журнал Технической Физики

View full text Add to dashboard Cite

Разработана экспериментальная установка для лазерно-акустической томографии биологических тканей. Проведены экспериментальные исследования фантомных образцов. Разработана программа для преобразования графических данных лазерно-акустической томографии в стандартный медицинских формат. Ключевые слова: лазерная ультразвуковая томография, формат DICOM, биологическая ткань, цифровая модель, графические данные.

show abstract

Section: проблемы формирования цифровых моделей по графической информunclassified

Преобразование Данных Лазерной Ультразвуковой Томографии В Медицинский Формат DICOM

Novikov¹,

Reshetov²,

Симонова³

et al. 2020

Журнал Технической Физики

View full text Add to dashboard Cite

show abstract

“…They are significantly harder to read, and especially determining the boundaries of the regions of interest is difficult. Because preoperative MR images are more detailed, several works [1][2][3][4][5][6][7] proposed methods to register intraoperative ultrasound and preoperative MR images. Some methods use a biomechanical model of the brain in addition to image processing techniques.…”

Section: Contextmentioning

confidence: 99%

“…If after the simulation the resection cavity is different from the original estimation, the model is run again with the updated resection cavity. The stereovision cameras only Most image processing methods 6,7 ignore the resection cavity in the algorithm: the registration between the pre-resection and during-resection images is done regardless of the resection cavity difference between the two images. In Riva et al, 6 a similarity metric (normalized cross-correlation) is used patch-wise to retrieve the transformation locally.…”

Section: Contextmentioning

confidence: 99%

“…The authors report that while good results were obtained for before-resection volumes, the method performed poorly with after-resection volumes. In Machado et al, 7 transformation invariant features are extracted from the images and used to perform the registration. After generating a set of possible correspondences, a gaussian mixture model is used to retrieve the most probable deformation.…”

Section: Contextmentioning

confidence: 99%

See 1 more Smart Citation

Automatic segmentation of brain tumor resections in intraoperative ultrasound images

Carton

Noble

Chabanas

2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

View full text Add to dashboard Cite

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.

show abstract

“…Furthermore, the effect of the outliers can be reduced by feature-based methods, limiting the registration only to corresponding elements. In [17], the authors proposed a feature-based registration approach where corresponding images features are computed in US pairs. Afterward, they used them to estimate a dense mapping through the images.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Purpose Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. Methods The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. Results Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. Conclusions We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.

show abstract

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Abstract: This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

Cited by 47 publications

References 52 publications

Преобразование Данных Лазерной Ультразвуковой Томографии В Медицинский Формат DICOM

Преобразование Данных Лазерной Ультразвуковой Томографии В Медицинский Формат DICOM

Automatic segmentation of brain tumor resections in intraoperative ultrasound images

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

Contact Info

Product

Resources

About