Author:
Kobets Andrew J.,Alavi Seyed Ahmad Naseri,Ahmad Samuel Jack,Castillo Ashley,Young Dejauwne,Minuti Aurelia,Altschul David J.,Zhu Michael,Abbott Rick
Abstract
Abstract
Background
Segmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa-related conditions, such as Chiari malformation, trigeminal neuralgia, post-operative pediatric cerebellar mutism syndrome, and Crouzon syndrome. Herein, we present a summary of the current literature on segmentation of the posterior fossa. The review highlights the various segmentation techniques, and their respective strengths and weaknesses, employed along with objectives and outcomes of the various studies reported in the literature.
Methods
A literature search was conducted in PubMed, Embase, Cochrane, and Web of Science up to November 2023 for articles on segmentation techniques of posterior fossa. The two senior authors searched through databases based on the keywords of the article separately and then enrolled joint articles that met the inclusion and exclusion criteria.
Results
The initial search identified 2205 articles. After applying inclusion and exclusion criteria, 77 articles were selected for full-text review after screening of titles/abstracts. 52 articles were ultimately included in the review. Segmentation techniques included manual, semi-automated, and fully automated (atlas-based, convolutional neural networks). The most common pathology investigated was Chiari malformation.
Conclusions
Various forms of segmentation techniques have been used to assess posterior fossa volumes/pathologies and each has its advantages and disadvantages. We discuss these nuances and summarize the current state of literature in the context of posterior fossa-associated pathologies.
Publisher
Springer Science and Business Media LLC
Reference82 articles.
1. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57. https://doi.org/10.1109/42.906424
2. Greenberg MS et al. (2020) Chapter 17.1: Chiari Malformations. In: Schwartz N, Hiscock T, eds. Handbook of Neurosurgery. 9th ed., Thieme 292–300
3. Bagci AM, Lee SH, Nagornaya N, Green BA, Alperin N (2013) Automated posterior cranial fossa volumetry by MRI: applications to Chiari malformation type I. AJNR Am J Neuroradiol 34(9):1758–1763. https://doi.org/10.3174/ajnr.A3435
4. Shenoy VS, Syringomyelia SR (2023) In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK537110/
5. Nishikawa M, Sakamoto H, Hakuba A, Nakanishi N, Inoue Y (1997) Pathogenesis of Chiari malformation: a morphometric study of the posterior cranial fossa. J Neurosurg 86(1):40–47. https://doi.org/10.3171/jns.1997.86.1.0040
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