Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis

Title

Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis

Creator

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

Description

Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G.

Source

Journal of Computer Science, Vol-20, No. 5, pp. 585-593.

Date

2024-01-01

Publisher

Science Publications

Subject

Cavity Segmentation; Enhanced U-Net; Post-Operative Brain MRI; Skull Removal; VGG16 Backbone

Coverage

Sobha X.P., 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; Hybrid Gold Open Access

Relation

ISSN: 15493636

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3844/JCSSP.2024.585.593

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194284446&doi=10.3844%2fJCSSP.2024.585.593&partnerID=40&md5=dd8a104fd6768b13e9b519e68bbde065