Neural-network-based automatic segmentation of cerebral ultrasound images for improving image-guided neurosurgery

Nitsch, Jennifer; Klein, Jan; Moltz, Jan Hendrik; Miller, Dorothea; Sure, Ulrich; Kikinis, Ron; Meine, Hans

doi:10.1117/12.2512289

Cited by 4 publications

(4 citation statements)

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“…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].…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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Purpose: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. Methods: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. Results: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49mm (± 1.55mm) to 1.36mm (±0.61mm). Moreover, the mTRE between volumes acquired before the opening the dura mater and at the end of the resection is reduced from 3.54mm (±1.75mm) to 2.05mm (±1.12mm). Conclusion: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…Segmenting structures in medical images has been widely studied, for which deep learning is the current state-of-the-art. 11 This has been applied to ultrasound images of the brain, to segment the midbrain, 12 sulci and falx cerebri , 13,14 tumor, 15 resection cavity. 16 However, multi-class segmentation models have been shown to obtain better results in other applications, 17,18 leveraging inter-class dependencies.…”

Section: Introductionmentioning

confidence: 99%

Multiclass segmentation of brain intraoperative ultrasound images with limited data

Carton

Noble

Lann

et al. 2021

Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling

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During tumor resection surgery, intraoperative ultrasound images of the brain show anatomical structures like the sulci , falx cerebri and tentorium cerebelli , as well as the tumor. After resection started, the resection cavity is also visible. These elements help with the localization and tumor resection, and can be used to register the preoperative MRI to intraoperative images, to compensate for the tissue deformation occurring during surgery. In this work, we compare single-and multi-class segmentation models for the sulci , falx cerebri , tumor, resection cavity and ventricle. We present strategies to overcome the severe class imbalance in the training data, and train a model with limited data. We show that a multi-class model may leverage inter-class spatial relationships and produce more accurate results than single-class models.

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“…Thus, it is no longer only used for registration tasks, but also for tissue classification, tracking and intra-op monitoring of patient and surgical processes, drug perfusion and decision support systems. In this setting, segmentation of brain structures can initialize image based registration [12] or assist brain shift and tracking error compensation [10] and thus provide interventional decision support. Nonetheless, segmentation of soft tissue in US, especially intracranial, is a demanding task for algorithms, as well as experts.…”

Section: Introductionmentioning

confidence: 99%

Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound

Demiray¹,

Rackerseder²,

Bozhinoski³

et al. 2019

Preprint

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Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support, the task of segmenting grey and white matter in cranial ultrasound is very challenging and has not been addressed yet. We train a multi-scale fully convolutional neural network simultaneously for two classes in order to segment real clinical 3D ultrasound data. Parallel pathways working at different levels of resolution account for high frequency speckle noise and global 3D image features. To ensure reproducibility, the publicly available RESECT dataset is utilized for training and cross-validation. Due to the absence of a ground truth, we train with weakly annotated label. We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error. To further improve results, we perform transfer learning using synthetic US data. The resulting method leads to excellent Dice scores of 0.7080, 0.8402 and 0.9315 for grey matter, white matter and background. Our proposed methodology sets an unparalleled standard for white and grey matter segmentation in 3D intracranial ultrasound.

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Neural-network-based automatic segmentation of cerebral ultrasound images for improving image-guided neurosurgery

Cited by 4 publications

References 4 publications

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

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

Multiclass segmentation of brain intraoperative ultrasound images with limited data

Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound

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