Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review

Title

Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review

Creator

Sobha X.P.; Sathish P.K.; Raju G.

Description

Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of postoperative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. 2024, Institute of Advanced Engineering and Science. All rights reserved.

Source

Bulletin of Electrical Engineering and Informatics, Vol-13, No. 5, pp. 3463-3469.

Date

2024-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Deep learning models; Fine-tuning and enhancement; Magnetic resonance images; Postoperative brain; Segmentation; U-net architectures

Coverage

Sobha X.P., Department of Computer Science, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India, Department of Computer Science, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India; Sathish P.K., Department of Computer Science, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Raju G., Department of Computer Science, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 20893191

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/eei.v13i5.7930

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