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PubMed Publications| Keyword search (4,163 papers available) |  |
"Brain tumor resection" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection | Pirhadi A; Salari S; Ahmad MO; Rivaz H; Xiao Y; | 36306056 PERFORM |
| Title: | Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection | ||||
| Authors: | Pirhadi A, Salari S, Ahmad MO, Rivaz H, Xiao Y | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/36306056/ | ||||
| DOI: | 10.1007/s11548-022-02770-5 | ||||
| Publication: | International journal of computer assisted radiology and surgery | ||||
| Keywords: | Brain shift; Brain tumor resection; Image registration; Intra-operative ultrasound; Landmark; Siamese network; | ||||
| PMID: | 36306056 | Category: | Date Added: | 2022-10-28 | |
| Dept Affiliation: |
PERFORM
1 Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. a_pirhad@encs.concordia.ca. 2 Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada. 3 Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. 4 Department of Electrical and Computer Engineering and PERFORM Centre, Concordia University, Montreal, Canada. 5 Department of Computer Science and Software Engineering and PERFORM Centre, Concordia University, Montreal, Canada. |
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Description: |
Purpose: In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety. Methods: We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages. An efficient 2.5D approach and an iterative re-weighted least squares algorithm are utilized to perform landmark-based registration for brain shift correction. The proposed method is validated and compared against the state-of-the-art methods using the public BITE and RESECT datasets. Results: Registration of pre-resection iUS scans to during- and post-resection iUS images were executed. The results with the proposed method shows a significant improvement from the initial misalignment ([Formula: see text]) and the method is comparable to the state-of-the-art methods validated on the same datasets. Conclusions: We have proposed a robust technique to efficiently detect matching landmarks in iUS and perform brain shift correction with excellent performance. It has the potential to improve the accuracy and safety of neurosurgery. |
