Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance

Title

Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance

Creator

Xavier P.S.; Sathish P.K.; Raju G.

Description

Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures. 2024 by the authors of this article.

Source

International journal of online and biomedical engineering, Vol-20, No. 5, pp. 133-149.

Date

2024-01-01

Publisher

International Federation of Engineering Education Societies (IFEES)

Subject

Attention-Enabled U-Net; post-operative magnetic resonance imaging (MRI); resection cavities; segmentation; VGG16 encoder

Coverage

Xavier P.S., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Sathish P.K., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Raju G., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 26268493

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3991/ijoe.v20i05.45609

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