Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis Subject The topic of the resource Cavity Segmentation; Enhanced U-Net; Post-Operative Brain MRI; Skull Removal; VGG16 Backbone Description An account of the resource 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. Creator An entity primarily responsible for making the resource Sobha X.P.; Sathish P.K.; Raju G. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-20, No. 5, pp. 585-593. Publisher An entity responsible for making the resource available Science Publications Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3844/JCSSP.2024.585.593" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/JCSSP.2024.585.593</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194284446&amp;doi=10.3844%2FJCSSP.2024.585.593&amp;partnerID=40&amp;md5=dd8a104fd6768b13e9b519e68bbde065" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194284446&amp;doi=10.3844%2fJCSSP.2024.585.593&amp;partnerID=40&amp;md5=dd8a104fd6768b13e9b519e68bbde065</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15493636 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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