Automatic Segmentation of Vestibular Schwannomas: A Systematic Review.
World Neurosurg
; 188: 35-44, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38685346
ABSTRACT
BACKGROUND:
Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more reliable and efficient for tracking their progression, especially given the irregular shape and growth patterns of VS.METHODS:
Various studies and segmentation techniques employing different Convolutional Neural Network architectures and models, such as U-Net and convolutional-attention transformer segmentation, were analyzed. Models were evaluated based on their performance across diverse datasets, and challenges, including domain shift and data sharing, were scrutinized.RESULTS:
Automatic segmentation methods offer a promising alternative to conventional measurement techniques, offering potential benefits in precision and efficiency. However, these methods are not without challenges, notably the "domain shift" that occurs when models trained on specific datasets underperform when applied to different datasets. Techniques such as domain adaptation, domain generalization, and data diversity were discussed as potential solutions.CONCLUSIONS:
Accurate measurement of VS growth is a complex process, with volumetric analysis currently appearing more reliable than linear measurements. Automatic segmentation, despite its challenges, offers a promising avenue for future investigation. Robust well-generalized models could potentially improve the efficiency of tracking tumor growth, thereby augmenting clinical decision-making. Further work needs to be done to develop more robust models, address the domain shift, and enable secure data sharing for wider applicability.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neuroma, Acoustic
/
Neural Networks, Computer
Limits:
Humans
Language:
En
Journal:
World Neurosurg
Journal subject:
NEUROCIRURGIA
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos
Country of publication:
Estados Unidos