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

Authors

DOI:

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

Keywords:

Attention-Enabled U-Net, Post-operative MRI, Resection Cavities, Segmentation, VGG16 Encoder.

Abstract


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.

Author Biographies

Sathish P. K., Christ (Deemed to be University)

Associate Professor at the School of Engineering and Technology, Christ Deemed to be University, Bangalore Campus. He holds a Ph.D. degree from Visvesvaraya Technological University, completed in 2019. He also has an AMIE certification and a Master's degree (M.Tech).His expertise lies in the fields of Computer Science, Artificial Intelligence, Image Processing, Computer Vision, and Pattern Recognition.

Raju G, Christ (Deemed to be University)

Professor in the Department of Computer Science and Engineering at Christ (Deemed to be University). He has a diverse educational background, with a Masters's degree in Physics, a Masters's degree in Computer Applications, and a Doctoral degree in Computer Science from the University of Kerala. Additionally, he completed his M. Tech in Computer Science and IT from Manonmaniam Sundaranar University, Tirunelveli. His primary research interests include Image Processing, Computer Vision, Data Science, Machine Learning, and Deep Learning. He  has published over 120 research articles and has supervised 24 Ph.D. scholars.

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Published

2024-03-15

How to Cite

Xavier P, S., P. K., S., & Raju G. (2024). Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance. International Journal of Online and Biomedical Engineering (iJOE), 20(05), pp. 133–149. https://doi.org/10.3991/ijoe.v20i05.45609

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Section

Papers